Lecture Notes in Earth Sciences Editors: S. Bhattacharji, Brooklyn G. M. Friedman, Brooklyn and Troy H. J. Neugebauer, Bonn A. Seilacher, Tuebingen and Yale
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3 Berlin Heidelberg New York Hong Kong London Milan Paris Tokyo
Michael S. Rosenbaum A. Keith Turner (Eds.)
New Paradigms in Subsurface Prediction Characterization of the Shallow Subsurface Implications for Urban Infrastructure and Environmental Assessment
123
Editors Michael S. Rosenbaum Professor of Geological Engineering Civil Engineering Division Faculty of Construction, Computing & Technology The Nottingham Trent University Nottingham, NGI 4BU, United Kongdom
[email protected] A. Keith Turner Professor of Geological Engineering Colorado School of Mines Golden, Colorado 80401, USA
[email protected] ”For all Lecture Notes in Earth Sciences published till now please see final pages of the book“ ISSN 0930-0317 ISBN 3-540-43776-2 Springer-Verlag Berlin Heidelberg New York
Cataloging-in-Publication Data applied for Bibliographic information published by Die Deutsche Bibliothek. Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliographie; detailed bibliographic data is available in the Internet at .
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer-Verlag Berlin Heidelberg New York a member of BertelsmannSpringer Science+Business Media GmbH http://www.springer.de © Springer-Verlag Berlin Heidelberg 2003 Printed in Germany The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Production: PRO EDIT GmbH, Heidelberg, Germany Typesetting: Camera ready by author Printed on acid-free paper 32/3141dn - 5 4 3 2 1 0
Preface This publication contains material presented at the Euroconference entitled "New Paradigms for the Prediction of Subsurface Conditions: Characterisation of the Shallow Subsurface and Implications for Urban Infrastructure and Environmental Assessment", together with ideas and inspiration freely shared amongst the delegates. The Euroconference was held at the Sol Cress conference centre, Spa, Belgium, 7-12 July 2001. This Conference, the 7thin a series (Turner, this volume), arose from a series of meetings which began over 10 years ago, as mentioned in the introduction to Part A (this volume), in Santa Barbara in California with NATO sponsorship. Then followed a move to Europe with a 3D GIs focus, to Wiirzburg and then to Freiburg (Germany), then with ESF funding to: Lucca (Italy), Canterbury (UK) and Kerkrade (The Netherlands). The conference offered young researchers an opportunity to explore crossdisciplinary applications of their specialised research knowledge and experience through participation in lectures, demonstrations, and poster sessions, as appropriate to their research. An important aspect of the conference was the time periods set aside for discussion of issues by smaller focus groups, and the subsequent reporting of these discussions to the entire group of participants. Each focus group was no larger than 10 participants, so that all had a chance to raise issues. Arrangements were made to assist groups in recording their discussions. The conference organisers assigned participants to these groups, and discussion group facilitators (chairs, secretaries and reporters) were appointed, thereby assuring that young researchers were heard during these discussions. The authors hope that the essence of the meeting has been successfully captured in this volume, enabling the delegates' deliberations to be shared with the wider community and encouraging the evolution of information technologies to better address user needs for subsurface characterisation.
Mike Rosenbaum A Keith Turner The editors
vi
Preface and Contributing Authors
Acknowledgements and Thanks Many people have helped enable this publication to materialise. We would especially like to acknowledge our thanks to:
Our financial backers: The European Science Foundation (ESF) and the European Union (EU). The Commission for the Management and Application of Geoscience Information (COGEOINFO) of the International Union of Geological Sciences (IUGS).
Our administrators: The ESF Secretariat, notably Caroline Grimont and Corinne Le Moal The Conference Secretary: Pamela Thompson IT Support: Jos Maccabiani Reporting and Co-ordinating the Discussion Groups: Robert Evans And, not least, the authors.
The European Science Foundation: This conference was sponsored by the European Science Foundation (Conference No: 2001-148) and the European Union under Research DG, Human Potential Programme, High-Level Scientific Conferences Contract HPCF-CT- 1999-00 125. The information presented is the sole responsibility of the author(s) and does not reflect the ESF or Community's opinion. The ESF and the Community are not responsible for any use that might be made of data appearing in this publication.
Citations: This publication should be cited as follows: Rosenbaum MS, Turner AK (eds) (2003) Characterisation of the Shallow Subsurface: Implications for Urban Infrastructure and Environmental Assessment. Springer-Verlag, Dusseldorf, 000-000 Individual papers within this publication should be cited as follows: Kontogianni V, Stiros S (2003) Experience from recent tunnels: Rock mass quality assessment based on predicted and observed deformation. In: Rosenbaum MS, Turner AK (eds) Characterisation of the Shallow Subsurface: Implications for Urban Infrastructure and Environmental Assessment, Springer-Verlag,Dusseldorf, 000-000
Preface and Contributing Authors
vii
List of contributing authors Allard, Ronald Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: r.allard@,nitu.tno.nl
Baynes, Fred Baynes Geologic 9 Chester Street, Subiaco, WA 6008 Australia E-mail: fredb(i3iinet.net.a~
Bolivar, Stephen Los Alamos National Laboratory, Risk Reduction & Env. Stewardship Division Los Alamos, P.O. Box 1663, New Mexico 87545 USA E-mail: bolivar@,lanl.gov
Bombien, Henning Bundesanstalt fur Geowissenschaften and Rohstoffe (NLfB) Stillweg 2,30655 Hannover Germany E-mail:
[email protected] Bremmer, Chris Netherlands Inst. of Applied Geoscience TNO - National Geological Survey PO 6012,2600JA Delft The Netherlands E-mail: c.bren~mer(a),nita.tno.nl
Cambou, Bernard Ecole Centrale de Lyon 36 Av. Guy de Collongue, BP 163,69131 Ecully cedex France E-mail: bernard.cambou@,ec-1yon.fr
Canepa, Julie
Los Alamos National Laboratory, Risk Reduction & Env. Stewardship Division
viii
Preface and Contributing Authors Los Alamos, P.O. Box 1663, New Mexico 87545 USA E-mail: canepa iulie(dlanl.gov
Chacon Montero, Jose Dpto. Ingenieria Civil, Universidad de Granada Edificio Politecnico, 18071 Granada Spain E-mail: jchacon(iugr.es
Coelho, Paulo A Lopes de F. Departamento de Engenharia Civil, Universidade de Coimbra Faculdade de CiEncias e Tecnologia Portugal E-mail: pac@,dec.uc.pt
Crosta, G.B.
Dip. di Scienze Geolog. e Geotec., Universita degli studi di Milano-Bicocca Piazza della Scienza 4,20126 MI-I Milano Italy
Culshaw, Martin G. British Geological Survey Kingsley Dunham Centre, Keyworth, Nottingham, NG12 5GG and The Nottingham Trent University Burton Street, Nottingham, NG1 4BU UK E-mail: m.culshaw~,bas.ac.uk
Czyzkowski, Bartosz
Faculty of Environmental Protection, Warsaw University of Technology Nowowiejska 20,OO-653 Warsaw Poland E-mail: Bartosz.Czyzkowski~is.pw.edu.pl
D'Agnese, Frank A. U.S. Geological Survey, Water Resources Division 520 North park Avenue, Suite 22 1, Tuscon, Arizona 857 19 USA E-mail:
[email protected] Preface and Contributing Authors
ix
Davison, Leslie
University of the West of England Faculty of the Built Environment, Coldharbour Lane, Bristol BS16 1QY UK
E-mail: leslie.davison(~uwe.ac.uk
de Mulder, Ed F.J. Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: e.demulder(dnitg.tno.nl
Dorries, Alison Los Alamos National Laboratory, Risk Reduction & Env. Stewardship Division Los Alamos, P.O. Box 1663, New Mexico 87545 USA E-mail: adorries@,lanl.gov
Evans, Robert Geohazards Group, The Nottingham Trent University Faculty of Construction, Computing and Technology, Newton Building, Burton Street, Nottingham NG 1 4BU UK E-mail: robert.evans@,ntu.ac.uk
Ferentinou, Maria Structural Mechanics Laboratory, National Technical University of Athens Rural and Surveying Engineering Dep., 9 Hervvn Polytecniou Street, Athens Greece E-mail: mferen@,mail.ntua.gr
Fookes, Peter G. Lafonia, 11A Edgar Road, Winchester, Hants SO23 9SJ UK
Groot, Richard formerly Geoinformatics Management, ITC Hengleostraat 99, P.O.Box 6,7500 AA Enschede The Netherlands now Gamma Resources Consultancy GENERAL DELIVERY, 2 10 Main Street, Wolfville, Nova Scotia Canada BOP 1XO
E-mail: groot@,nammaresources.com
x
Preface and Contributing Authors
Haasnoot, Jacco
Witteveen+Bos Consulting Engineers P.O. Box 233,7400 AE Deventer The Netherlands E-mail: Jacco.Haasnoot@,nzliin.nl
Hack, Robert
Engineering Geology Section, Centre for Technical Geosciences, ITC Kanaalweg 3 2628 EB Delft The Netherlands E-mail:
[email protected] Hella, Pirjo Fintact Oy Hopeatie 1 B, FIN-00440 Helsinki Finland E-mail: pirio.
[email protected] Jackson, Peter
British Geological Survey Keyworth, Nottinghamshire, NG 12 5GG UK E-mail: peter.iackson@b~s.ac.uk
Jimenez Rodriguez, Rafael Dpto. Ingenieria Civil, Universidad de Granada Edificio Politecnico, 1807 1 Granada Spain E-mail: ri
[email protected] Kaalberg, Frank Witteveen+Bos Consulting Engineers P.O. Box 233,7400 AE Deventer The Netherlands E-mail: Frank.Kaalberg~lnzliin.nl;kaalberg0,planet.nl
Karoutsos, Vagelis Institute of Chemical Engineering and High Temperature Chemical Processes, Foundation for Research and Technology Hellas, Stadiou Street, Platani, P.O.Box 1414, GR 26500 Patras Greece
Preface and Contributing Authors
xi
Kochanek, Kmysztof Faculty of Environmental Protection, Warsaw University of Technology Nowowiejska 20,OO 653 Warsaw Poland E-mail:
[email protected] Kontogianni, Villy Patras University, Department of Civil Engineering Geodesy and Geodetic Applications Laboratory, 26500 Patras Greece E-mail:
[email protected] Kooijman, Jan Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands
Kosters, Elisabeth formerly Geoinformatics Management, ITC Hengleostraat 99, P.O.Box 6,7500 AA Enschede The Netherlands now Gamma Resources Consultancy, GENERAL DELIVERY, 2 10 Main Street Wolfville, Nova Scotia Canada BOP 1XO E-mail: kosters@,,aammaresources.com
Maccabiani, Jos Engineering Geology Section, Delft University of Technology Faculty of Civil Engineering and Geosciences, P.O. Box 5028,2600 GA, Delft The Netherlands E-mail: j osm@,bart.nl
Maurenbrecher, Pieter M. Engineering Geology Section, Delft University of Technology Faculty of Civil Engineering and Geosciences, P.O. Box 5028,2600 GA, Delft The Netherlands E-mail: p.m.maurenbrecher@,ta.tudelft.nl
Meijers, Pieter Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: p.meiiers@,nitg.tno.nl
xii
Preface and Contributing Authors
Nasser, Khalil revision Incorporated 1320 Detroit Street Suite 1, Denver CO 80206 USA E-mail: knasser@,revisioninc.com
Nawalany, Marek Faculty of Environmental Protection, Warsaw University of Technology Nowowiejska 20,OO 653 Warsaw Poland E-mail: marek.nawalany@,is.pw.edu.pl
Netzel, Holger Structural Mechanics Section, Delft University of Technology Faculty of Architecture, Berlageweg 1, Postbus 5043,2600 GA, Delft The Netherlands E-mail: holg;er.netzel@,nzlijn.nl
Ngan-Tillard, Dominque J.M. Engineering Geology Section, Delft University of Technology Faculty of Civil Engineering and Geosciences, P.O. Box 5028,2600 GA, Delft The Netherlands E-mail: D.J.M.Ngan-Tillard@,ta.tudelft.nl
Numrnela, Jorrna Fintact Oy Hopeatie 1 B, FIN 00440 Helsinki Finland E-mail: jorma.nummela@,fintact.fi
O'Brien, Grady M. U.S. Geological Survey, Water Resources Division 520 North park Avenue, Suite 22 1, Tuscon, Arizona 857 19 USA E-mail: gmobrien@,usgs.gov
Preface and Contributing Authors
xiii
Ozmutlu, Sen01 Engineering Geology Section, Centre for Technical Geosciences, ITC Kanaalweg 3 2628 EB Delft The Netherlands E-mail: ozmutlu@,itc.nl
Pagnier, Henk Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: h.pannierm),nitg.tno.nl
Prissang, Rene
Freie Universitat Berlin, FR Geoinforrnatik Malteserstr. 74-100 (Haus D), D 12247 Berlin Germany E-mail: prissang@,zedat.fu-berlin.de
Pumodihardj o, Ardie Ecole Centrale de Lyon, Lab. de Tribologie and Dynamiques des Systkmes Biitiment G-8, Avenue Guy de Collongue, B.P. 163-F, 69 131 Ecully cedex France E-mail: apurwodiOec-ly011.fr
Ritsema, Ipo Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: i.ritsema@,nitg.tno.nl
Rosenbaum, Michael S. Geohazards Group, The Nottingham Trent University Faculty of Construction, Computing and Technology, Newton Building, Burton Street, Nottingham NG 1 4BU UK E-mail: mike.rosenbaum@,ntu.ac.uk
Sakellariou, Michael Structural Mechanics Laboratory, National Technical University of Athens Rural and Surveying Engineering Dep., 9 Hervvn Polytecniou Street, Athens Greece E-mail: mgsakel@,rnail.ntua.vr
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Preface and Contributing Authors
Saksa, Pauli Fintact Oy Hopeatie 1 B, FIN 00440 Helsinki Finland E-mail: pauli.saksa@,fintact.fi
Sirnrnelink, Erik Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: h.simmelink@,nitg.tno.nl
Sinicyn, Grzegorz Faculty of Environmental Protection, Warsaw University of Technology Nowowiejska 20,OO 653 Warsaw Poland E-mail: Grzegorz.Sinicvn(~is.pw.edu.pl
Smith, Ian British Geological Survey Kingsley Dunham Centre, Keyworth, Nottingham, NG12 5GG UK E-mail: i .smith@,bgs.ac.uk
Sobisch, Hans-Georg Geologisches Institut der Universtat zu Koln Abteilung Quartargeologie, Ziilpicher Str. 49a, 50674 Koln Germany E-mail: Hans-Georg.Sobisch@,uni-koeln.de
Sterlacchini, Simone CNR Centro Geodinamica Alpina e Quaternaria Via Mangiagalli 34,20 133 MI-I Milano Italy E-mail : sirnone.sterlacchini@,unimib.it
Stevens, Rodney Department of Earth Sciences, Goteborg University Box 405, SE 405 30 Goteborg Sweden E-mail: stevens@,gvc.gu.se
Preface and Contributing Authors
xv
Stiros, Stathis Patras University, Department of Civil Engineering Geodesy and Geodetic Applications Laboratory, 26500 Patras Greece
Theodoropoulou, Maria
Department of Pharmacy, University of Patras GR 26500 Patras Greece
Trykozko, Anna Interdisciplinary Centre for Mathematical and Computational Modelling Pawinskiego 5a, 02 106 Warsaw Poland E-mail: aniat@,icm.edu.pl
Tsakiroglou, Christos Institute of Chemical Engineering and High Temperature Chemical Processes, Foundation for Research and Technology Hellas, Stadiou Street, Platani, P.O.Box 1414, GR 26500 Patras Greece E-mail: ctsakir@,t,terpsi.iceht.forth.gr
Turner, A. Keith TU Delft, Civil Engineering and Geosciences PO Box 5028,2600 GA Delft The Netherlands also Department of Geology & Geological Eng., Colorado School of Mines Golden, CO 8040 1-1887 USA E-mail: kturner@,mines.edu
van Wees, Jan-Diedrick Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015, 3508 TA Utrecht The Netherlands E-mail: j.vanwees@,nit~.tno.nl
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Preface and Contributing Authors
Veldkamp, J.G. Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015, 3508 TA Utrecht The Netherlands E-mail: j.veldkam~@,nita.tno.nl
Verhoef, Peter N.W. Engineering Geology Section, Delft University of Technology Faculty of Civil Engineering and Geosciences, P.O. Box 5028,2600 GA, Delft The Netherlands E-mail: p.n.w.verhoef@,ta.tudelft.nl
Versseput, Rob Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: r.versseput@,nita.tno.nl
Vuurens, Robert Engineering Geology Section, Delft University of Technology Faculty of Civil Engineering and Geosciences, P.O. Box 5028,2600 GA, Delft The Netherlands E-mail: vuurens@,amx.net
Wassing, Brecht B.T. Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: b.b.t.wassing(3Jnitg.tno.nl;wasbreuk@,wxs.nl
Zanchi, Andrea Dipart. di Scienze dell'Ambiente, Universita degli studi di Milano-Bicocca Piazza della Scienza 1,20126 MI-I Milano Italy E-mail: andrea.zanchi@,unimib.it
Zijl, Wouter Netherlands Institute of Applied Geoscience TNO- National Geological Survey Princetonlaan 6, P.O. Box 80015,3508 TA Utrecht The Netherlands E-mail: w.ziilG3nita.tno.nl
Contents Preface Acknowledgements Contributing Authors
v vi vii
Part A Fundamentals
1
1 Characterisation of the shallow subsurface: implications for urban infrastructure and environmental assessment
3
Mike Rosenbaum 1.1 1.2 1.3 1.4 1.5
The Context The Objectives The Issues The Challenges The Conference
2 Bridging the gap between geoscience providers and the user community
3 4 5 5 6
7
Martin Culshaw 2.1 Context 2.2 What is meant by geology? 2.3 Geoscience information providers 2.4 The development of the geological map 2.4.1 Before 1835 2.4.2 After 1835 2.5 The users and their needs 2.6 The evolution of the geological map to meet new user needs 2.6.1 Digital geological maps 2.6.2 Applied geological mapping 2.7 The general public 2.8 Conclusions
3 Definition of the modelling technologies
7 8 8 9 9 12 14 16 17 19 23 24
27
A. Keith Turner 3.1 The Context 3.2 Principles of geological characterisation 3.3 Initial Developments 3.4 Categories of GIS 3.5 Development of Geoscientific Information Systems 3.6 Capabilities of GeoScientific Information Systems 3.7 Advances in hardware and software technology 3.8 Classes of Geoscientific Information System 3.9 Towards a desirable 3D data structure 3.10 The geological characterisation process
27 27 28 29 30 31 31 32 35 38
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Contents
4 New paradigms for geoscience information management
41
Khalil H. Nasser, Stephen Bolivar, Julie Canepa, Alison Dorries 4.1 Introduction 4.2 Challenges facing the geoscience community 4.3 The need for new paradigms 4.3.1 The challenge 4.3.2 Organisational alignment to meet stakeholder/client needs 4.3.3 Re-engineering the work process 4.3.4 An implementation strategy 4.4 Case Study – the LANL ER Project 4.4.1 ER Project goals and needs 4.4.2 Business process analysis 4.4.3 Success factors 4.4.4 Strategic plan development 4.4.5 Creating the IM team 4.4.6 Re-engineering the business processes 4.4.7 Implementation of the hardware/software infrastructure 4.4.8 Conceptual system design 4.4.9 Database design 4.4.10 Data migration and cleanup 4.4.11 Software definition and development 4.5 Conclusions and the Challenges Ahead
41 41 43 43 43 44 45 48 48 48 49 50 50 51 52 52 54 54 56 57
Part B End-User Requirements
59
5 Putting the user first: implications for subsurface characterisation
61
A. Keith Turner 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9
Introduction Defining user requirements The problem with traditional products Developing new products to satisfy modern users Geo-Objects and subsurface modelling Solutions to improve efficiency Implementing new information technology Putting the User first Conclusions
6 What are the end-user issues? Settlement risk management in underground construction
61 61 62 62 63 65 65 67 68
69
Frank Kaalberg, Jacco Haasnoot, Holger Netzel 6.1 6.2 6.3 6.4 6.5 6.6
The North-South Line Metro Project Geological Characteristics of the Subsurface Creating a Three Dimensional Ground Model Numerical Modelling of Tunnelling Effects The Integrated Boring Control System (IBCS) End-User issues
69 71 72 76 79 82
Contents
7 Data requirements for geo-environmental management: end-user and producer perspectives for a harbour
xix
85
Rodney L. Stevens 7.1 Introduction 7.2 End-user and provider perspectives 7.3 Harbour site investigations 7.3.1 Documentation Methods 7.3.2 Sediment parameter priorities 7.3.3 Multi-functional considerations 7.4 Conclusions
8 A decision support system for groundwater: water management issues for opencast mining
85 85 88 88 90 92 93
97
Marek Nawalany, Grzegorz Sinicyn, Krzysztof Kochanek, Bartosz Czyzkowski Introduction Impact on groundwater Making decisions A case study from the Konin region of Poland Conclusions
97 98 99 99 102
Part C Model Construction and Visualisation
103
9 Visualisation: are the images really useful?
105
8.1 8.2 8.3 8.4 8.5
René Prissang 9.1 Introduction 9.2 What is Visualisation? 9.3 Geoscientific visualisation 9.3.1 Data Visualisation 9.3.2 Shape visualisation 9.3.3 Volume visualisation 9.3.4 Process Visualisation 9.4 Examples 9.5 Caveats 9.6 Conclusions
10 Numerical schemes for process simulation: software for coupling pattern recognition to process modelling
105 105 106 106 107 107 109 109 112 113
115
Wouter Zijl, Anna Trykozko 10.1 Introduction 10.2 Cell complexes and their properties 10.3 Process modelling 10.4 Paradigms 10.5 Discrete analogues of the paradigm equations 10.5.1 The curl side 10.5.2 The div side 10.6 Discrete flux-based process models 10.7 Numerical example: homogenisation of flow parameters 10.8 Conclusions
115 116 117 118 119 119 120 121 123 126
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Contents
11 Regional subsurface models and their practical usage
129
Hans-Georg Sobisch, Henning Bombien 11.1 11.2 11.3 11.4
The Context Modelling methods Boundary conditions Outlook
12 Slope stability estimation using GIS
129 129 130 130
135
Maria Ferentinou, Michael Sakellariou 12.1 12.2 12.3 12.4 12.5 12.6
The Context The relevance of GIS to slope stability Development of a landslide hazard assessment tool Case Study: Diakopto, Greece Landslide hazard zonation Conclusions
13 Using 2D GIS for the 3D reconstruction of the Corno Zuccone sackung, Val Taleggio (Italy)
135 135 136 138 138 139
141
Andrea Zanchi, Simone Sterlacchini, Crosta G.B. 13.1 13.2 13.3 13.4 13.5
Context The Corno Zuccone landslide Using 2D GIS for 3D modelling in g0cad Reconstructing the Corno Zuccone landslide Discussion
14 Sedimentation modelling: data analysis in Rotterdam Harbour
141 141 142 142 143
151
Robert Vuurens, Pieter Maurenbrecher 14.1 14.2 14.3 14.4 14.5 14.6 14.7
Introduction Sedimentary environments Harbour study areas Data mining Data-driven modelling Parameter evaluation Conclusions
15 Fluid flow in fractured formations
151 151 152 153 154 156 158
161
Christos Tsakiroglou, Maria Theodoropoulou, Vagelis Karoutsos 15.1 Introduction 15.2 Laboratory experiments 15.3 Theoretical flow of a fluid 15.3.1 Flow through a pore 15.3.2 Flow through a single fracture 15.4 Discussion 15.5 Conclusions
161 162 164 164 166 167 168
Contents
16 Effect of strain-softening on settlement due to tunnelling in soft soil
xxi
173
Ardie Purwodihardjo, Bernard Cambou 16.1 Introduction 16.2 Constitutive models 16.2.1 Mohr-Coulomb model 16.2.2 Strain-Softening Model 16.2.3 Identification of parameters 16.2.4 Validation of the model 16.3 Deformation analysis 16.3.1 Input Data 16.3.2 Computation procedure 16.4 Conclusions
173 173 173 174 175 175 179 179 179 187
Part D Dissemination Strategies
189
17 Dissemination of geoscience data: societal implications
191
Ed F.J. de Mulder, Jan Kooijman Introduction Data as a treasure Data collection and storage Data dissemination in the European context Technical feasibility versus societal expectation Discussion
191 192 194 198 198 199
18 An economic model for the dissemination of national geological survey products and services
201
17.1 17.2 17.3 17.4 17.5 17.6
Richard Groot, Elisabeth Kosters 18.1 Introduction 18.2 The Public Interest 18.3 Economic characteristics of geological surveys 18.3.1 The assets 18.3.2 What constitutes the monopoly of a geological survey within an ICT environment? 18.3.3 Is it in the public interest to allow a geological survey to compete in the market for products C(l)? 18.3.4 What should be the price-setting principles in relation to the strategic goals? 18.4 Management 18.5 The role of regulation 18.6 Regulatory reform 18.7 Conclusions
19 Access to national geoscience repositories using digital geoscience spatial models
201 203 205 205 206 209 209 211 212 213 214
217
Ian Smith 19.1 The Context 19.2 The role of a Geological Survey Organisation 19.3 Strategies for preservation of information
217 217 219
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Contents
19.4 The Digital Geoscience Spatial Model 19.5 The requirements for a Digital Geoscience Spatial Model 19.6 Conclusions
20 Dissemination and visualisation of earth system models for the Dutch subsurface
219 221 223
225
Jan-Diederik van Wees, Rob Versseput, Ronald Allard, Erik Simmelink, Henk Pagnier, Ipo Ritsema, Pieter Meijers 20.1 The Context 20.2 Digital database of the Dutch subsurface 20.3 Dissemination and visualisation system 20.3.1 The Web-based dissemination component 20.3.2 The Client-based 3D visualiser 20.4 Capabilities of the Geo3DJViewer 20.5 Future development of the viewer 20.6 Added value for geoscience interpretation 20.7 Conclusions
225 226 226 226 227 229 232 233 233
Part E Property Prediction
235
21 Total Geological History: a web-based modelling approach to the anticipation, observation and understanding of site conditions
237
Leslie Davison, Peter Fookes, Fred Baynes 21.1 The Context 21.2 Total Geological History 21.3 Initial models 21.4 Case history: Emerson Green 21.4.1 Developing a site specific model for Emerson Green 21.5 Geological Environment Matrix (GEM) 21.5.1 Digital data 21.5.2 Knowledge based systems 21.5.3 Data base systems 21.6 A middle course 21.6.1 Codifying knowledge 21.6.2 The role of case histories 21.6.3 Navigation 21.6.4 Communication issues 21.7 A solution 21.7.1 Interface design 21.7.2 Content 21.8 Summary
22 3D modelling system for ground engineering
237 238 238 238 239 239 240 241 241 242 242 243 243 243 244 244 245 246
253
Senol Ozmutlu, Robert Hack 22.1 22.2 22.3 22.4
Introduction Case Study 1: Foundations Case Study 2: Tunnels Conclusions
253 253 255 259
Contents
23 Rock mass quality assessment based on deformation: experience from recent tunnels
xxiii
261
Villy Kontogianni, Stathis Stiros 23.1 23.2 23.3 23.4 23.5 23.6
Introduction Design approach Prediction of tunnel convergence Monitoring tunnel convergence Tymfristos tunnel and the equivalent GSIe Conclusions
24 Improving subsurface parameter estimation through guiding geophysical inversions with a priori geological data
261 261 262 263 265 267
269
Peter Jackson 24.1 Introduction 24.2 Testing Resistivity Inversion 24.3 Constructing a constraint compatible with ‘step’ changes in resistivity 24.4 A field case study 24.5 Conclusions
269 272 275
25 Characterisation of void space utilising pore network models
283
279 279
Christos Tsakiroglou Introduction Pores Fractures Characterising the micro-porous matrix of a clay till SEM image analysis Conclusions
283 283 284 284 286 287
26 Assisting data interpretation: the cone penetration test
293
25.1 25.2 25.3 25.4 25.5 25.6
Jos Maccabiani, Dominique Ngan-Tillard, Peter Verhoef 26.1 Introduction 26.2 The INURCS project 26.3 CPT interpretation 26.4 Automated segmentation of CPT sequences 26.5 Segment parameters 26.6 Fuzzy clustering 26.6.1 Traditional fuzzy c-means 26.6.2 Extended fuzzy c-means (‘E-FCM’) 26.7 Conclusions
293 293 294 294 296 297 297 297 298
Part F Managing Uncertainty
301
27 Impact of geoinformatics on the emerging Geoscience Knowledge Integration Paradigm
303
Frank D’Agnese, Grady O’Brien 27.1 Introduction 27.2 Addressing society’s emerging concerns
303 304
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Contents
27.3 From data to wisdom 27.4 The need for a GeoInfrastructure 27.4.1 Integrative protocols 27.4.2 Enabling geoscientists 27.4.3 GeoInfrastructure Value 27.4.4 Implementation of the GeoInfrastructure 27.5 The development of a ‘GeoPro’ structure 27.6 The need for standards 27.7 Conclusions
28 Assessment of uncertainties in volume and yield estimations of clastic deposits
304 306 307 307 307 308 309 311 312
313
Brecht Wassing, Hans Veldkamp, Chris Bremmer 28.1 Introduction 28.2 Site investigation 28.3 Results of the site investigation 28.3.1 General geology of the Maas deposits 28.3.2 Geology of the southern and northern pilot sites 28.3.3 Sources of inaccuracy of data 28.4 Geostatistics for estimating volume and grading 28.5 Conclusions
29 Uncertainty with characterisation of soils
313 313 314 314 314 315 316 321
323
Paulo Coelho 29.1 The Context 29.2 Geotechnical features of natural soils 29.3 Case study: Baixo Mondego 29.3.1 Geotechnical properties 29.3.2 Stress history and consolidation properties 29.4 Practical implications
30 Analysing uncertainty when modelling geological structures
323 323 325 325 326 328
331
Pauli Saksa, Pirjo Hellä, Jorma Nummela 30.1 Introduction 30.2 Sources of uncertainty 30.3 Methodology 30.4 Case Study 30.4.1 Main site characteristics 30.4.2 Applying a statistical model 30.4.3 Uncertainty ratings 30.4.4 Uncertainty aspects concerning the Olkiluoto site model 30.5 Conclusions
331 331 332 333 333 335 335 337 339
31 Uncertainty characterisation: reliability methods for slope stability
341
Rafael Jiménez Rodríguez, José Chacón Montero 31.1 Introduction 31.2 Model formulation
341 342
Contents 31.3 Statistical characterisation of input variables 31.4 Reliability of a single component 31.4.1 Analytical methods 31.4.2 Simulation methods 31.5 Reliability of a parallel system 31.5.1 Simulation results 31.5.2 FORM and SORM results 31.6 Conclusions
xxv 344 345 345 345 349 350 351 352
Part G The Way Ahead
357
32 Current themes, issues and challenges concerning the prediction of subsurface conditions
359
Rob Evans 32.1 Challenges 32.2 The Research Challenges 32.3 Responses to the Representation Challenge 32.4 Responses to the Cognition Challenge 32.4.1 Responsibility for interpretation 32.4.2 The role of a geoscientist 32.5 Responses to the Uncertainty Challenge 32.5.1 Uncertainties in the Data 32.5.2 Uncertainties in the Modelling 32.5.3 Communicating Uncertainty 32.5.4 Time and scaling 32.6 Responses to the Data Challenge 32.6.1 Data storage 32.6.2 Data dissemination 32.6.3 Standardisation of data formats and models 32.6.4 Importance of providing metadata 32.7 Responses to the Simulation Challenge 32.7.1 Auditing and checking models 32.7.2 Simulation of reality 32.8 Responses to the Provider’s Challenge 32.8.1 What can geoscience produce? 32.8.2 Incorporating knowledge within the model 32.8.3 New techniques 32.9 Responses to the User’s Challenge 32.10 Geo-education 32.11 Conclusions
33 The Global Geoscientific Knowledge Network: overcoming the impediments to subsurface characterisation
359 359 361 363 363 364 365 366 366 367 368 368 369 369 370 371 371 372 372 373 373 374 374 375 376 377
379
A. Keith Turner, Mike Rosenbaum 33.1 Introduction 33.2 The role of technological innovation 33.3 Societal demands on the geosciences 33.3.1 The role and status of National Geological Surveys 33.3.2 Current international initiatives and programmes 33.4 Current research in geovisualisation 33.4.1 The geovisualisation research agenda
379 380 383 383 384 385 386
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33.4.2 The ICA Research Network Operations 33.5 Impediments to subsurface characterisation 33.6 Strategy for overcoming impediments 33.7 Research concerning technological issues 33.8 Networking to support the research agenda 33.9 Research agenda for developing protocols 33.10 Establishing the future international network 33.11 Potential network partners
Index
387 388 388 389 390 390 391 392
395
1 Characterisation of the shallow subsurface: implications for urban infrastructure and environmental assessment Mike Rosenbaum The Nottingham Trent University, Nottingham, UK
1.1 The Context Civil and geotechnical engineering and environmental applications depend on a precise definition of the shallow subsurface. The very shallow urban underground environment, where many infrastructure and utilities elements are located, presents very difficult characterisation problems. Characterising the deeper subsurface is important for groundwater resource evaluations, environmental contamination assessments, and for some infrastructure applications such as underground storage facilities. Subsurface geological modelling became an economic and technological reality in the late 1980's, when competing 3D geoscientific information systems were the subject of considerable research and evaluation. Integration of modelling within decision-support systems is now occurring rapidly, spurred by the evolution of the Internet and Web-browser technologies, hardware and software developments, expanded information transmission capabilities, data visualisations, and modern data base concepts. Despite considerable research being devoted to subsurface characterisation over the past decade, the pace of technological innovation has outstripped the ability of many potential users to evaluate and adopt promising new characterisation methods. Subsurface characterisation demands the prediction of geological, geotechnical and geomechanical properties based on economic and timely exploration methods. It usually faces a lack of information about the geological framework, the engineering properties and behaviour of the materials, and the existing stress field at a given location. Even the most sophisticated of the new methods leave some uncertainty in geological interpretation. Uncertainty in geologic interpretation has been evaluated, primarily within the petroleum industry, by a variety of techniques including fuzzy logic, fuzzy clustering, and probabilistic methods (including Monte Carlo simulation and conditional probabilities). Process response models, stochastic models, and neural nets have all been investigated as part of decision support system design. M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 3–6, 2003. c Springer-Verlag Berlin Heidelberg 2003
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Investigations in the shallow subsurface differ from those conducted for petroleum or mineral exploration. Shallow investigations generally require much higher feature resolution, more precise property matching, and the ability to differentiate conditions in a noisy environment; the essence of Engineering Geology. Engineering Geophysics has evolved as a sub-speciality to provide high-resolution imaging to depths of a few tens of meters. Environmental Geochemistry has followed suit. Traditional sampling methods are being supplemented by innovative penetrometer techniques for soft soils, and by directional drilling and coring methods. The very shallow urban underground environment, where many infrastructure and utilities elements are located, presents the most difficult characterisation problems. Characterising slightly deeper portions of the shallow subsurface is important for groundwater resource evaluations, environmental contamination assessments, and for some infrastructure applications such as underground fluid storage and deeply buried facilities. It is becoming increasingly clear that achieving a more complete understanding of the Earth's geological systems will require ever increasing degrees of integration of data, insights, and the perspectives derived from many subdisciplines. The considerable research efforts that have been devoted to subsurface characterisation over the past decade offer a means for integrating spatially referenced data, and provide important visualisation and interpretation tools. The current systems are based on fundamental data models developed in the 1970's and 1980's - an era with a very different computer technology from that available today. The changes in computer technology, and the evolution of our concepts of information management, dictate a new evaluation of these fundamentals. Yet an important question remains: how can the use of these subsurface characterisation methods be made easier and more productive?
1.2 The Objectives The aim has been to provide the basis for a research agenda for the next decade concerning cross-disciplinary issues with respect to the roles of geological modelling and computing science in ground characterisation. The Objectives are to: Explore cross-disciplinary applications of geological modelling and computing science in ground characterisation through dialogue between providers and users Establish how subsurface characterisation methods can be made easier and more productive Establish a network and one or more centres that provide the critical mass needed to realise the full potential of co-operation, synthesis, integration, and innovation for better predicting subsurface conditions Encourage young researchers to explore cross-disciplinary applications of their specialised research knowledge and experience.
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1.3 The Issues The chief impediments to greater use of subsurface geological modelling are: the lack of fully developed 3D and 4D oriented mathematical, cognitive, and statistical spatial analysis tools the inability of available systems to accurately depict the natural variability of geological features, or to represent uncertainties related to interpretation of spatial configuration or properties the lack of broad access to specialised tools for exploring and modelling geological systems. Although many such tools have been developed for the oil industry, they are too expensive or commercially sensitive for all but a few organisations to procure the lack of comprehensive examples that might break the 'scientific inertia'. Many scientists know about quantitative subsurface characterisation methods, but do not embrace them because they wonder if the investment in time, effort, and research funds to deal with the existing software tools will result in better science.
1.4 The Challenges One way to motivate basic research is by posing grand research challenges. The conference is designed to address seven challenges that represent the fundamental scientific questions that will drive subsurface characterisation research in the next decade: The Representation Challenge: To find ways to express the infinite complexity of the geological environment using the binary alphabet and limited capacity of a digital computer. The Cognition Challenge: To achieve better transitions between cognitive and computational representations and manipulations of geological information - to recognise what is seen. The Uncertainty Challenge: To find ways of summarising, modelling, and visualising the differences between a digital representation and the real phenomenon. The Data Challenge: To respond to the increasing quantity of data being collected and archived, to the increasing sophistication of the data content, and to the demands of complex analyses. The Simulation Challenge: To create simulations of geological phenomena in a digital computer that are indistinguishable from their real counterparts
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The Provider's Challenge: The role of the geoscientist as an information provider (through the use of models) is placing increasing emphasis on the requirement to be customer-orientated; the focus is on the 'end use' of the knowledge that is being imparted - and should the Provider interpret the information? Where does responsibility lie? The User's Challenge: Ensuring the end use of a material or product is made clear - to facilitate a 'performance basis' to ensure that the model is both 'fit for purpose' and 'fit for end-use' - and should the User interpret the information? Where does responsibility lie?
1.5 The Conference This volume has arisen from a Conference, held in 2001 at Spa, which was organised to explore the seven major Challenges just set out. Both information providers and information users were invited to explore how the rapidly evolving and maturing data acquisition and information technologies can better address specific user needs for subsurface characterisation. The conference utilised keynote speakers from appropriate industries, while a core group of researchers interacted with different applications specialists. An important aspect of the conference was the time periods set aside for discussion of issues by smaller focus groups, and the subsequent reporting of these discussions to the entire group of participants. It is hoped that the essence of the meeting has been successfully captured in the chapters which follow, whereby the delegates' deliberations concerning the evolution of information technologies to better address user needs for subsurface characterisation are shared with the reader.
2 Bridging the gap between geoscience providers and the user community Martin Culshaw British Geological Survey and Nottingham Trent University, UK
2.1 Context Though geology is now a fully recognised academic subject, its origins were as much driven by need (and greed) as detached enquiry. The industrial revolution that shaped today's world was, in large part, driven by the use of mineral resources to manufacture new products and provide the necessary energy to fuel industry and improve communications. The environmental consequences of this revolution were largely ignored in the rush to create wealth and develop society. Consequently, geologists had a key role in the location and development of the mineral resources. However, as easily located resources were depleted, geologists had to develop their understanding of the earth to enable them to locate new sources of minerals. This led to the foundation of university geology departments and the setting up of geological surveys. Today, these twin pillars of geological advance still dominate geoscientific research and, until recently, with the start of the information technology revolution, their outputs would have been readily recognisable to lgth century geologists. However, digital systems have completely altered the nature of what geoscientific information can be provided. The user is no longer solely the professional geologist working in mining or oil and gas companies. Today, everybody is a user because now we are concerned far more with the consequences of our actions for our environment. We are concerned about the impact of contamination on our health, about whether ground conditions will affect the stability of our homes and the cost of insuring it and about whether climate change will increase the risk from geohazards. Traditional geological maps and academic publications do not meet these new needs. However, the increasing availability of information in digital form and the improving access to it have revolutionised what can be provided to a wide range of users. The paper will explore this historical change both in the user demand and in the ability of geologists to provide information in useable forms. This is illustrated with reference to the development of the geological map over the last two hundred years and how it has changed from a brightly coloured rectangular piece of paper to a means, for example, for the calculation of insurance premiums.
M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 7–26, 2003. c Springer-Verlag Berlin Heidelberg 2003
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2.2 What is meant by geology? Put simply, in dictionary style, geology is the study of the Earth. It is interesting to note that the definition in the Concise Oxford Dictionary is wrong, defining geology as "Science of the earth's crust, its strata, and their relations and changes." All geologists would surely agree that geology covers the whole of the Earth, not just the crust. However, the study of the Earth has many aspects to it. It is still popularly believed that the main job of the geologist is to find energy, metalliferous and industrial mineral and groundwater resources. This is because these natural resources originate within the Earth and have to be located. Indeed, it could be argued that geology and the discovery of mineral resources were hugely influential in the development of the industrial revolution. One example illustrates this - the presence of coal, iron and building materials around Ironbridge and Coalbrookdale in Shropshire, that enabled Abraham Darby to develop the first successful smelting of iron ore using coke, which was derived from local coal. Subsequently, his grandson built the first iron bridge, after which the village was named. The growth of geology to satisfy the increasing demand for resources by the industrialised world has seen geologists develop their research to help meet these demands. They have done this by seeking to increase our understanding of what happened through geological time so that we can build three-dimensional geological models to assist us in predicting the location of economically viable resources.
2.3 Geoscience information providers While it is clear that geology is of key importance in wealth creation, it is also very relevant to the improvement of the quality of life. The industrial revolution, which the availability of geological materials helped to fuel, also produced an environmental legacy with which we are only just starting to come to terms. As we move into an era in which future development will have to be increasingly sustainable, so geologists are having to provide society with the information needed to achieve this. Society needs to be assured that our groundwater is safe to drink, that our buildings and infrastructure are safely constructed, that the ground that we use for building, recreation and cultivation is not contaminated and that we do not exacerbate natural geological hazards. It is hard to know whether the first geologist was a consultant or an academic, whether slhe was concerned with solving a practical problem or simply interested in the rocks and soils around them and wanted to know more. Some of the first geologists looked at the composition of rocks (petrologists and mineralogists). Others were concerned to understand the way life had evolved through examination of the fossil record (palaeontologists), while a further group sought to classify the rocks in terms of their age and how they came to be where they are today (stratigraphers). Such geologists still practice today. It could be argued, that these geologists are mainly interested in geology for its own sake, and are less con-
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cerned with whether what they do has any practical application. Their work is often described as pure geology, in the sense that it is not influenced by considerations other than the pursuit of knowledge. Other geologists are more concerned with the relevance of geology to improving people's lives. Such scientists are usually referred to as applied geologists. Maybe this separation between those geologists pursuing knowledge and those trying to apply that knowledge wouldn't matter too much if it hadn't been for the development of a third strand for the provision of geoscientific information - geological surveys. Their creation was deemed necessary because governments perceived that it was of national importance to know what mineral resources a country possessed and in what quantities. Academic geologists were unwilling or unable to provide this geoscientific information systematically on a geographical basis, and consulting geologists did not have the financial resources to do so. Consequently, government organisations were created to meet this need and the first task of these new geological surveys was to provide geological maps covering whole countries. However, the geological map was not 'invented' by geological surveys; its development can be traced back over 3000 years. Very few scientific disciplines have developed this three-fold division in terms of who provides society with the outputs of that discipline. Geology, however, is different from most other sciences in that most of what is done has a spatial dimension to it. All natural samples come from a definable point within the earth and all have got there as a result of a series of processes acting over very long periods of time. Geology is highly dependent, therefore, on being able to locate an observation on a rock in three dimensions. The geological map was devised to enable the visualisation of the relative positions of different rock samples and how they were related. Consequently, the geological map, in all its forms, has been the primary geological output for almost 200 years. It is not surprising, therefore, that in January 2000, Simon Conway Morris of the University of Cambridge, writing in Earth Heritage (a magazine on geological and landscape conservation produced by English Heritage, Scottish National Heritage, the Countryside Council for Wales and the Royal Society for Nature Conservation) put the geological map as No. 1 on his list of geology's ten key episodes of the Millennium.
2.4 The development of the geological map 2.4.1 Before 1835 It is probable that the earliest known topographic maps were produced by the Babylonians. A city plan dating from about 2200 BC is held in the Istanbul Museum while the British Museum has a clay tablet showing a representation of the world and dating from about the 5Ih century BC (Tooley, 1978). The earliest geologically-related map or section may be the Turin Papyrus that dates from about 1320 BC and shows an Egyptian gold mine. Other maps and sections were no doubt produced from time to time though few have survived. Most of these related to the working of minerals and are really mining maps that relate to the local geol-
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ogy. Examples include those produced by Georgius Agricola in 1556. These show the traces of metalliferous ore veins and the mines sunk to exploit them. Similar minerallmining maps were produced elsewhere in Europe. For example, maps of various Bohemian mine districts survive from the 15" and 16" centuries (Urban, 1984) and a map showing copper-bearing vein systems in Hosenberg, Germany is dated 1574 (Langer, 1984). Wilson (1985) suggested that the "concept of cartographic illustration of geological phenomena" could be traced back to Martin Lister who, in 1684, produced maps showing sands and clays. According to Milanovsky (1984), the first true geological maps were compiled of parts of Western Europe by Guttard, Desmarest and Fiichsel in the second half of the isth century. In Russia the first map was by Lebedev and Ivanov in 1789 - 1794 for parts of the Transbaikalian Region. In Britain, the Scot John Clerk produced a number of accurate sketches of parts of Scotland in the period 1785 to 1788 (Craig, 1984). These sketches were not discovered until 1976. They included a section of Arthur's Seat and Salisbury Crags in Edinburgh, a section through the Southern Uplands and a map of Glen Tilt in Tayside. William Maton produced a "Mineralogical map of the Western Counties of England" in 1797. However, relatively large-scale, truly geological cross sections were being produced much earlier than this. For example, Strachey published cross sections of the Somerset coalfield in 1725 (Figure 2.1).
Figure 2.1. Cross sections of the Somerset coalfield published by Strachey (1725)
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These maps from the latter half of the lgthCentury differed in one significant manner from later maps: they showed the rocks in terms of their composition (lithology and petrology) rather than their age (stratigraphy). While this may seem to be of little consequence to the non-geologist, it is only by being able to distinguish rocks by their age as well as their composition that we can work out the relationships between different groups of rocks and hence begin to predict what will be found in places where the rocks are not exposed. It was the use of fossils to determine relative age and to correlate between different rock outcrops that allowed the development of stratigraphy. This major advance was brought about by William Smith in Britain and by Cuvier and Brongiart in France, though Werner in Germany had also developed similar approaches. These few examples seem to indicate that geological map making was not a significant activity but that when practised, the maps produced related to the exploitation of minerals. It is not surprising, therefore, that, given this practical background, the beginnings of geological map making, as we now know it, coincided with the industrial revolution and its exploitation of natural resources and the rapid development of transportation networks. As the industrial revolution was strongly carried forward in Britain, it is also not surprising that many of the foundations of modern geological science were also laid down there. Amongst the pioneers was William Smith. An interesting fact about William Smith is that he was not a geologist; rather he was a surveyor and civil engineer. He started work as a surveyor on the Somerset Coal Canal near Bath. Here the rocks were well exposed in the hilly landscape and also in quarries. Smith was able to trace sequences of rocks because he recognised groups of fossils that characterised rocks of a particular age. In 1797 he produced a small geological map of the area around Bath based on the relative age of the strata (the stratigraphic succession). It was the first map of this type and it used a topographic map as its base, a principle that has continued to the present. Smith was only concerned with the practical applications of his newly discovered approach. He struggled to write down descriptions of his work and he never joined the Geological Society of London, founded in 1807. In 1815, Smith published a geological map of England using topographic base maps provided by John Cary. The map measured 2 m by 3 m and consisted of fifteen sheets. Smith also drew a number of cross sections across southern Britain. Subsequently, Smith produced twenty one county geological maps at a larger scale. These examples demonstrate that the production of geological maps was largely driven by firstly, the exploitation of minerals and secondly, as the industrial revolution increased the production of heavy goods, the development of transportation routes. Around six thousand kilometres of canals were built in the second half of the 18'" Century and the first half of the 1 9 Century. ~ In the USA, the drivers for geological development were similar. Friedman (2001) has shown how the involvement of geologists in the planning for, and building of, the Erie Canal (New York State) between 1810 and 1825 was important not only for its success but in helping to lay the foundations for geology in the USA. The work included a reconnaissance survey by geologist Stephen Van Rensselaer (17931871) (who was a member and President of the Board of Canal Commissioners for
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many years) and a geological survey that resulted in a cross section along the canal route, by Amos Eaton (1776-1842). 2.4.2 After 1835 The Ordnance Survey (then the Ordnance Trigonometrical Survey) had been formed in 1791 to produce topographic maps of Britain and (subsequently) Ireland. When Major General Thomas Colby took over as Superintendent of the Ordnance Survey in 1820, he intended that the topographic maps should serve as the base for geological survey. Captain J Pringle was asked to form a geological branch in Ireland in 1827 but the work lapsed when it became clear that the surveyors were not adequately qualified to supply the geological information. Partly at the suggestion of Sir Roderick Murchison, the President of the Geological Society, a geologist, Henry De la Beche was recruited to the Ordnance Survey and in 1835 the Geological Ordnance Survey was formed. Richard Griffith, a Fellow of the Geological Society took over the work in Ireland and in 1845 the Geological Survey of Great Britain and Ireland was formed as an independent organisation (Owen and Pilbeam, 1992). As with the maps of William Smith and others, Murchison anticipated that the mapping would have practical benefits for "agriculture, mining, road-making, the formation of canals and railroads, and other branches of national industry." With the development of stratigraphy and the formation of the Geological Survey, the long process of systematically mapping the UK began. Bush (1974) has pointed out that the history of stratigraphy and the development of geological mapping are inextricably linked. The latter could not have developed without the former. By the time that the whole of Britain is mapped to an acceptable standard, around one hundred and seventy years will have passed. However, it is important to understand what the maps show and why, despite the optimistic beginnings, these maps have failed to provide the modern world with the practical benefits that were anticipated. This failure is as much to do with changes in science and engineering and in society generally, as it is to do with the geological mapmakers. As described earlier, the geological map is an interpretation of what we expect to find at the surface when agricultural soils, vegetation and buildings are removed. However, what is shown may not actually be there! Data is gathered from outcrops, by interpretation of the shape of the ground surface, from temporary excavations, borehole logs, geophysical surveys and remotely sensed data from aeroplanes and satellites. The information is plotted in its correct position in relation to the topography and the national co-ordinate system and is finally interpreted according to the latest geological theories and the knowledge and experience of the geologist. In other words, the geological map is not simply a collection of facts. Two modern geologists provided with the same sets of data would probably produce two different geological maps of the same area. It is not surprising, therefore, that maps made over a hundred years apart should look dramatically different because not only has the amount of data increased but the knowledge of geological processes, and the theories to explain them, have changed too. Bush (1974) gave an example of two maps of the Canadian Shield made in 1928 and
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1958. The two maps are radically different in appearance because they are based on different theories of the origin of granite. A further difference relates to the type of geological material that is actually shown on the geological map. Conventionally, geological deposits are divided into three broad groups for mapping purposes, based primarily on age. The oldest are 'solid rocks,' that is those formed before the Quaternary Period; then there are 'superficial' (or 'drift') materials formed during the Quaternary and partially concealing the solid rocks; finally there are 'anthropogenic' deposits such as backfilled quarries, embankments and the remains of mineral workings. In the 19' Century, maps showed the 'solid rocks' but very little else. Geological Survey geologists in Great Britain, such as Clough, Peach and Home, were leaders in the development of the science as a result of their geological mapping in the Scottish Highlands. Peat might be depicted as well as alluvium (river valley deposits) but glacial deposits were ignored. In the 2othCentury superficial deposits were added, though difficulties in working out stratigraphic sequences has limited the degree of complexity shown until recently. However, mapping techniques have remained fairly traditional and simple until relatively recently. In the last ten to twenty years, deposits relating to human activity have started to be added but, again, the depiction is limited because we have not yet fully worked out a stratigraphic system to show the relationships between the deposits themselves and how they were formed. So, on the face of it, the modern geological map differs little, in principle, from that produced in the earliest days of geological surveys. Is this why the geological map failed to achieve Murchison's aim of practical use? Certainly, by the end of the lgthcentury there is evidence that engineers and geologists were not thinking in the same way. Glossop (1969) pointed out that from about 1850 onwards, geology became increasingly neglected by the civil engineering profession. Professor Keith Turner in his inaugural address to the Technical University of Delft (the Netherlands) (Turner, 2000) quotes Boyd Dawkins from his James Forrest Lecture to the Institution of Civil Engineers, London in 1898:
"Geology stands to engineering in the same relation as faith to works.. .. The success or failure of an undertaking depends upon the physical conditions which fall within the province of geology, and the 'works' of the engineer should be based on the ffaith' of the geologist. "
While Dawkins clearly appreciates the importance of geology to civil engineering, he does not appear to show much acceptance of geology as a science. The use of the word 'faith' could be interpreted as a throw back to the geological debates of the 18" and early 1 9 Centuries. ~~ At this time, there was growing concern about the biblical account of the formation of the Earth and this caused major disagreement between the proponents of the Neptunist and Plutonist theories. The former theory, espoused by Werner (1750-1817) in Germany, proposed that rocks had formed by sedimentation from a primordial ocean. These were exposed when the waters retreated to form continents. Noah's flood overwhelmed the continents and added new rock layers. Subsequent volcanic activity disrupted the continents and added more sediment. Later floods added more layers. In the Plutonist theory it was proposed that rocks were formed in fire. It was originally proposed by Abbe Anton Moro (1687-1750) on the basis of a study of volcanic islands. This alterna-
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tive to Werner's ideas was taken up by James Hutton (1726-1797) who proposed that the surface of the earth was subject to two basic processes in which rocks were worn away by weathering and erosion and then re-formed and uplifted by heat and pressure. A summary account of this and other early theoretical geological controversies is given by Johnston (2000). Dawkins' ambiguity about the work of geologists is amplified by another quotation provided by Turner (2000). This one, by Cornelius Lely, Minister of Works in the Netherlands, relates to a landslide on a railway embankment in 1892:
"...the Government has made inquiries from a few geologists who know the local situation, with the purpose of informing the engineers about the special qualities of the soils there. .. Of course it is not certain that the advice of the geologists could be or had to be followed.. . The building of a railway is not the work that you can trust to a geologist.. ."
It would appear, then, that the geological map, and the geologists that made them were not providing the advice and information that the founders of the Geological Survey of Great Britain and Ireland had intended. Or at least, engineers, and others, were not convinced of the value of geology. Why was this? One answer might be that the geological map, because it is, in itself, an interpretation by geologists, is not readily understandable by non-geologists. So, very quickly, the geological map had become an end in itself, rather than a way of making geological information and interpretation readily available to users. Academic geologists in our universities did little to alter this direction as they became increasingly interested in 'pure' geology and soon forgot its practical roots.
2.5 The users and their needs Geologists collect data in the laboratory, on site and in the field. They organise the data and make it available to others, so creating information. They interpret the information using their expertise so producing knowledge, which they communicate to others. For academic geologists, many of whom are 'pure' scientists, the principal outputs of their work are academic publications and presentations at conferences. The main users of their work are other geologists, both academics and consultants. For most of their existence, the main users of geological survey outputs were governments and academic and consulting geologists. The principal output was the geological map. These geologists readily understood the map, even though, as indicated above, other professional users struggled to make sense of it. Consulting geologists, whether employed within companies or brought in from outside, had the job of interpreting the geology for the use of the client or company. In the mineral resource industry, this process worked well because geology was a large part of what the companies were about. Their function is to find commercially viable minerals, process them and sell them and they employ many geologists. Over hundreds of years the relationship between the academic geologist trying to understand geological processes, the survey geologist providing spatially referenced information and the consulting geologist using these two outputs to help them locate mineral resources has worked reasonably well in creating wealth.
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However, as we have got richer we have become increasingly concerned about the quality of our lives. We no longer accept 'Acts of God' as inevitable. If our houses are damaged or fall down because of landslides or swelling and shrinking clays we want to know why, we want to reduce or eliminate the risk of financial loss and we want our buildings and structures to resist the effects of geological hazards. We are concerned not only to have water to drink and wash with but also that this water is clean and does not cause us to fall ill. We no longer accept that the waste products of our society can be dumped anywhere; we want reassuring that the disposal sites are safe, that they will not leak and pollute the places where we live. We welcome improvements in our urban environment but we insist that the residues left from previous activities are removed or sterilised. We want our activities to become more sustainable so that we do not consume all the resources but ensure that we leave a viable world for our children. The task of ensuring these quality objectives are met falls not to geologists but to other professional groups such as environmental scientists, engineers, planners, developers, financiers, surveyors, insurers, estate agents, lawyers and politicians. These non-geological professionals all require information and knowledge about the ground to enable them to perform their functions as developers of the built and natural environment and regulators of that development. However, the geological outputs that served the mineral resource industry so well have failed to be of much use these groups. Also, these groups have largely failed to realise how dependent they are on a sound appreciation of the ground conditions and, as a result, have employed few geologists within their organisations. As was indicated above, even civil engineers, who have a long history of working with geologists, still seem to have either a lack of understanding of the importance of geology or a lack of confidence in the ability of geologists to provide that knowledge. In the UK, it is still more likely that a site investigation will be specified by a structural engineer or an architect, than by an engineering geologist or a geotechnical engineer. It can be argued, that one of the main reasons for this state of affairs is that the geologist has not yet adapted to meet these new needs and that the outputs, as epitomised by the geological map, are inappropriate for this group of users. For geological surveys that produced the maps one of the main problems was that while medium and small scale geological maps were of great value to resource geologists, environmental concerns tended to be much more site specific and maps were needed at larger scales. Consequently, the role of Surveys in relation to geotechnical engineering, waste disposal, contaminated land, geohazard mitigation and, particularly planning, was less clear.
2.6 The evolution of the geological map to meet new user needs In the UK, two events were hugely influential in changing the form of the geological map to meet the needs of the new user groups. First, in 1948, the British Geotechnical Society was formed and the journal 'Gkotechnique' published. In his forward to the first part Terzaghi (1948) stressed the importance of engineering
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geology and emphasised the need for observation as well as theory in civil engineering. These events led to the development of engineering geology as an important sub-discipline of geology in the UK and the establishment of four Master of Science postgraduate courses in engineering geology in the 1960s and 70s (at Imperial College, London and Leeds, Durham and Newcastle Universities). The Engineering Group of the Geological Society was formed in 1964 and in 1967 the British Geological Survey (then the Institute of Geological Sciences) set up an Engineering Geology Unit. In 1972, A Working Party of the Engineering Group of the Geological Society published a report on the preparation of engineering geological maps and plans and on soil and rock classification (Anon. 1972); this was followed by a more theoretical report on engineering geological maps and a further report on soil and rock classification, both by the International Association of Engineering Geology (Anon., 1976, 1981). These reports established international standards for applied mapping. The second significant event was the publication in 1972 of the Rothschild Report into the funding of science in British research centres and surveys. This report recommended that approximately 30% of the core funds be transferred from the centres and surveys to central government departments which would then 'commission' work back from the centres and surveys. Previously, the amount of applied geological mapping carried out by the British Geological Survey had been minimal. Most mapping was of the traditional stratigraphic type. As far as the development of geological maps was concerned, two significant commissions were placed. Though work had begun before Rothschild, the Department of Trade and Industry (DTI) took over funding of the geochemical survey of Britain (G-Base). This involved the collection of stream samples on a regular grid basis starting in the northernmost parts of Scotland and systematically working southwards. Samples were analysed for a range on elements (mainly metals) and element distribution maps were published. The objective was to provide background levels of these elements to assist in the identification of potential mineral resources. During the work (which has now reached the English Midlands) the emphasis switched from the identification of resources to environmental concerns over the distribution of potentially contaminating elements. In addition, urban centres, that had initially been excluded, were covered but at higher sampling densities. This change from a resource-based to an environmental-based approach has been highly significant to the development of geological maps in the UK in the latter part of the 2othCentury. The second important commission was from the Department of the Environment for engineering geological mapping of the site and hinterland of the then proposed third London airport in south east Essex. A suite of more than forty maps and sections of a wide variety of types was produced (Anon., 1977) and experiments made into some forms of digital geological contouring (Coe and Cratchley, 1979). The intended users of the report included both civil engineers and planners. This work led, a few years later, to a fifteen year programme of applied geological mapping of urban areas for planning purposes. The outputs of this programme have been described by Culshaw et al., (1988, 1990) and Smith and Ellison (1999). However, the key point about it was that the maps were specifically produced to meet the needs of the planner, as specified by planners. An addi-
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tional output was a regional landslide mapping project in the South Wales Coalfield that identified a specific problem, relevant to planning in that area (Conway et al., 1980). These applied geological maps were very varied to meet the user need. They formed the basis for the subsequent blossoming in the 1990s, when computer power and programmes had developed to address the demands of spatial data, of a whole new range of digital maps. Some examples of this new world of geological maps are discussed below.
2.6.1 Digital geological maps It has been indicated above that the basic stratigraphical map has remained largely unchanged in its content since the lgthcentury. However, the advent of computer hardware and software able to deal with the large files associated with maps has changed the nature, and potential, of these maps dramatically. A map consists of points, lines and polygons. Points on a map represent a location that can be defined in three dimensional space, that is, it can be located in relation to some arbitrary point of origin. For example, the equator defines zero degrees of latitude (northings and southings), the Greenwich meridian defines zero degrees of longitude (eastings and westings) and sea level defines elevation (height above or below sea level). On a topographic map grid co-ordinates and contours are ways of defining a point location. A line links two points. On a topographic map a road might be represented by a line. A polygon is an area of any shape wholly enclosed by a line. A field with its hedge-line represented by a line is an example of a polygon. On geological maps, points might be locations where measurements were made or samples collected, lines might represent faults and polygons are areas underlain by one stratigraphic or lithological unit. Modern computer software allows us to hold point, line and polygon data digitally. Consequently, we are able to hold all aspects of a geological map in digital form. In addition to the points, lines and polygons, the software also allows us to hold other information that we could display as text, data tables, photographs, diagrams etc and which can be attached to the point, line or polygon. We can call up this additional information when we use the digital geological map. On a paperbased map this additional information is referred to as the 'key' or 'legend.' However, the amount of additional information that can be provided is limited by the area of paper available to print the information. With a digital map, the amount of additional information that can be included is limited mainly by the time taken to input and check the data and the capacity of the computer hardware and software to store and process it. This ability to hold additional information in a digital map system opens up new possibilities. A digital map that is simply a display on a computer screen of what could also be presented on paper does not represent much of an advance. The geological map differs fundamentally from a topographic one. Unlike a topographic map, which is a scaled down representation of a part of the Earth's surface, the geological map is a two dimensional representation of a three dimensional object - the ground beneath the surface. Furthermore, only a tiny amount of the sub-
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surface is observable by the geological mapmaker. Consequently, the map is not a representation but an interpretation of what is in the Earth. So, the geological map is an interpretation of the three-dimensional geology and, as such, is a synthesis of a lot of different data types. Sometimes a paper-based map will include a cross section of the geology, which is an interpretation of the geology on a vertical plane. Because of the lack of space, only a few sections can be printed on paper and these sections may not be in the places of interest to the map reader. If the computer-based map simply reproduces these cross sections on a screen little has been added. However, the ability to hold additional information allows us the possibility of producing a cross section in any direction that we chose. By adding information from boreholes, for example, we can create cross sections that link these boreholes together. In most of the major urban areas of the UK thousands of boreholes have been sunk in the last fifty years. The British Geological Survey holds the logs of around 900 000 of these boreholes. Consequently, there is a huge resource of information that can be made available, provided that we can find the resources to digitise and check it. A further advantage of the digital geological map is that it can be updated regularly. Paper-based maps, if updated, can only be made available by reprinting. This is expensive and also makes the existing unsold maps redundant. However, it is likely that many decades will pass between updates. For some remote areas, the current map may be up to 150 years old! With the digital map this situation changes. The map can be updated every day and when a user requires a copy they can have the latest version. If the map changes, it is easy for them to obtain the updated version, either in digital form, or printed onto paper. Of course, the user is no longer restricted to receiving a map of a rectangular area. Any outline can be specified, such as a local authority boundary.
2.6.2 Applied geological mapping Perhaps the most important benefit of the digital geological map is that it allows the geologist to recast the basic geological information into forms that are understandable to new non-geological users. Two examples are presented here, one a suite of applied geological maps intended primarily for planners and civil engineers, the other an information system for use by insurers. Both have their basis in the old fashioned stratigraphical map and neither could have been easily produced without the availability of digital technology. Neither is now state of the art even though both are less than ten years old.
As indicated earlier, in the late 1970s a programme of applied geological mapping was begun by the then Department of the Environment. The first project covered Bristol and the last (in 1996) the Bradford Metropolitan area. The penultimate project covered the Wigan Metropolitan Borough. The output consisted of two reports and ten maps. A technical report described the geological data collected, discussed the implications of the geological conditions for the area and presented the infor-
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mation in a comprehensive and structured format. This report was supplemented by a database that indexed the various relevant sources of geological data for Wigan. Nine of the ten maps accompanied this report. The content of these maps indicates how the requirement of geological maps has changed in the last twenty years. Two of them are consistent with the traditional style of litho-stratigraphic maps. These maps, showing the bedrock (solid) geology and the superficial (drift) geology, underpin the other maps, forming the basis for the derivation of many of them. In other words, many of the other maps use some, or all, of the linework from the stratigraphic maps but then describe the different areas enclosed by the lines (polygons) in ways that are appropriate to the theme being covered. The themes covered by the nine maps are indicated in Table 2.1, together with an explanation of the nature of the theme and the intended users. The themes covered indicate the nature of the geological issues that are important in Wigan. Elsewhere, other issues might be more important and so alternative or additional maps might be produced. For example, in Bradford, where similar work was carried out (Waters et al., 1996) landslides are a potential problem. As a consequence, mapping focussed on this problem and the number of known landslides increased from around twenty to over two hundred. A map showing the extent of landsliding in relation to the steepness of the ground was included as one of the thematic maps.
Table 2.1 Content and principal users of thematic geological maps of Wigan (Source: Forster et al., 1995)
Theme Bedrock geology Superficial geology Distribution of pits, boreholes and site investigations Hydrogeology Mineral resources Distribution of made and worked ground Previous and present industrial uses Engineering geology Shallow mining
Content Extent and litho-stratigraphy of bedrock Extent, thickness and lithostratigraphy of superfi-
Examples of users Geologists and geologically literate professionals Geologists and geologically literate professionals
Locations of subsurface boreholes, pits and indirect measurements Surface and ground-water features, water abstraction points and aquifers Potential and exploited mineral resources Extent and types of made ground, waste materials, landfill and other modified ground Potentially contaminating and general industrial land use, past and present Geotechnical characteristics and likely engineering behaviour Extent of opencast and subsurface mine workings, mine
Geologists and geologically literate professionals Planners, water companies, waste disposal companies Planners, mineral companies Planners, geotechnical and geo-envir&mental engineers Planners, developers, financiers geotechnical and geoenvironmental engineers Planners, geotechnical and geo-environmental engineers Planners, developers, geotechnical and geo-
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environmentalengineers
WWro bedruck IS exposed at t h aurlocn ~ or where superflcla mntw~nlIS more than 5m
IIIIL~ the erylnwnng r m l Is shown as a solld rnlntrr Whrrn s l r o ~ r f o a material l Is lesr than 5m rn l i 8 1 r k n Gstrips 0 : the abpermnt Crlynmr:ng unll colou- are .nrf!rw~.vsuO wlrll srrlol's rhow,ng ' twllndprly~ql ! ! 4 m k arglreerlmg umt s cnlrm Tlw superflcla c r q n c c r l l g unlli nl:nwv . I t :ho surlac3 may c o l r w P m k s uI. U< ilu m d ~ r l a nby. @her aliorlr8 rl n r q r w p w q u m 5 ,I..CICS~ fdc!.l 1s urr~naowd.
Figure 2.2. Extract from the BGS applied geological map for Wigan - ground conditions (1995) [Crown Copyright British Geological Survey, 19951
The second report provided a guide to the ground conditions in Wigan for nongeological users, mainly planners. This report was written largely by planners, rather than geologists. It provided an analysis of ground conditions in the context of the planning and economic factors that operate in the Wigan area. This analysis considered particularly: the extent of derelict land the means of, and proposals for, remediation of such land the impact of adverse ground conditions on development costs
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the relationship between ground conditions, economic development policies and potential grant aid. The second report was accompanied by the tenth map that showed the key geological factors relevant to planning and development. These factors were: abandoned shallow mineworkings the potential for contamination of land by past and present industry, including landfill sites the potential for groundwater contamination mineral resources major faults that might act as pathways for the transmission of gases and liquids. The ten maps in the whole report illustrate well the way in which the traditional geological map can be used to produce new geological maps that provide a wide range of information to a range of users (Figure 2.2). 2.6.2.2 Geohazard maps for the insurance industry
The late 1980's and early 1990's was a period of abnormally low rainfall across many parts of the UK, but particularly in south east England. This area is underlain by a number of stratigraphic units that consist mainly of clay, in particular the London Clay that underlies large parts of London and its hinterland. Clays tend to swell (increase in volume) during wet weather and shrink (reduce in volume) during dry periods. As a result of the prolonged period of dry weather the clays in south eastern England shrank more than usual. The shrinkage was exacerbated by the increased moisture requirement of trees and shrubs during the dry weather. The excess shrinkage of the ground removed support to the foundations of lightweight structures, particularly houses, causing damage to both foundations and super-structure. In the period 1989 to 1991, insurance companies lost around £1.5 billion in paying out claims. Swelling and shrinkage of clay soils are not the only hazards that cause damage to property and financial loss. Dissolution of gypsum in the Ripon area has caused losses of several million pounds while the Holbeck Hall landslide on the coast just south of Scarborough destroyed a hotel worth about E2M. Landsliding at Nefyn on the north coast of Gwynedd, North Wales has not only destroyed two houses but also killed one person and seriously injured another on 2 January 2001. Collapse of abandoned mineworkings has destroyed or damaged houses in Reading, Newcastle and Edinburgh in the last few years while long-abandoned mine shafts are regularly discovered adding to the cost of development by the need to safely cap them. These losses led insurance companies to demand an information system that could help them reduce losses by enabling them to set premiums at an appropriate level for the geological hazards found in each part of the country. The principle by which insurance largely operates is that the many pay for the misfortunes of the
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few. The system is based on the idea of social justice. However, the principles of natural justice suggest that those who are at higher risk should pay more than those at lower risk. Consequently, the premium paid by an individual is based on the exposure to risk of a group of individuals. The problem is to group together individuals so that the premiums paid reflect that level of risk. In terms of the risk of building damage from geological causes this means that what is required is a system that identifies the degree of susceptibility of geological hazards for defined geographical areas. A convenient way to do this is on the basis of address and, in particular, the areas defined by the postcode. Each part of the postcode (for example, NG12 5GG) represents a different size area enclosing smaller defined areas. The largest division is the postcode area represented by 'NG.' There are about 120 postcode areas in Britain. The next level is the postcode district (NG12). There are almost 3 000 of these. Each postcode district is divided into one or more sectors (NG12 5) of which there are over 9 000. Finally, each sector is divided into unit postcodes, represented by the full postcode (NG12 5GG). There are almost 1.6 million of these. As insurers classify addresses by postcode anyway and postcode boundaries are available digitally and regularly updated, they form the ideal basis for an address-based geohazard information system. To provide the information needed by insurers, the geology, as represented on the lithostratigraphical map was interpreted in terms of susceptibility to six geohazards: landsliding shrinkage and swelling dissolution cambering and gulls shallow undermining soft, compressible soils. Hence, a map was produced, in digital form, showing the extent of ground susceptible to each of the six hazards. However, such a map does not distinguish between areas of lesser and greater susceptibility to each hazard. To provide this analysis a quantitative 'total hazard rating' was determined for each of the 9 000+ postcode sectors. This was done by taking into account the local geological conditions within each sector using geological data, knowledge and experience. The result was a database containing information for each postcode sector. Each geohazard was analysed separately and the results of the analysis combined to produce the final sector rating taking into account possible interactions between geohazards. The sector rating values fall in a range between 0 (no geohazard) and about 180 (high susceptibility to geohazards). The rating values can be plotted on a digital map by establishing a series of classes representing rating value range (for example, 0-20, 20-40, etc.). Each class can then be represented by a different colour or shading. Each company can decide the level of premium that it wishes to charge
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depending on the geohazard rating for the postcode sector. Insurers can also determine their level of exposure (their potential liabilities) by comparing the locations of the properties insured with the variation of geohazard rating. By providing the information in digital form users can combine the geohazard data with other spatially-defined data (for example, the distribution of fires, or burglaries) to derive a more equitable approach to buildings insurance.
2.7 The general public The discussion above has focussed mainly on geological and non-geological professionals. However, a third group of users of geological information is developing - the general public. A minority of the public has always been interested in geology as the number of local geological societies testifies. Fossil hunting has been a popular past-time for many generations. In recent years the ever-expanding number of television channels has resulted in geology having increased coverage. It would seem that the most popular programmes are those covering fossils and those highlighting geohazards. Indeed, during the writing of this paper in May and June 2001, a topical and mainstream current affairs programme devoted a quarter of its one hour output to increased landslide activity and coastal erosion in Britain. Another mainstream channel presented several one hour live programmes on searches for fossils (particularly dinosaurs) on the Isle of Wight. At the same time, the increasing availability of access to the Internet has led to an increasing demand for information about land, particularly with regard to property transactions. Geological surveys and others are increasingly able to provide digital interpreted information with regard to geohazards and foundation conditions for the whole of their national territories. This information is then licensed to commercial information providers who combine it with other spatially-defined information and make it available over the internet either free (at a very simple 'yesfno' entry level) or at fairly low cost (a few tens of Euros). The information is often accessed through entry of a postcode (zip code) rather than a full address. As there are over a million housing transactions in the UK each year it seems likely that the demand for this sort of geoscientific information will continue to grow. However, the provision of such information to the general public puts an onerous obligation upon the information supplier. To avoid potential charges of negligence, the information system must be kept up to date as new information becomes available. Also, experience over the last decade has shown that while, at first, the geologist can provide more and better information than the public demands, expectations increase quickly and may very soon outstrip what is available. What has happened is that with the availability of fast digital systems and investment in the digitisation of information gathered over hundreds of years has enabled geological surveys and others to 'cash-in' on their historical databases. However, increasing awareness and hence demand has brought about a realisation that in all those hundreds of years we have not necessarily been collecting the right sort of information to meet modern demands. This is not surprising if it is remembered that geological surveys were created to collect information for min-
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era1 resource identification, rather than to resolve environmental problems. Consequently, a new phase of geological surveying and data collection must begin to meet these demands. In the past, the user priority in providing geoscientific information was probably: 1" academic geoscientists, 2' professional geoscientists, 3" other professionals, 41hthe general public. If geoscience is to continue to receive government support for research and training, then perhaps this priority has to be reversed. We have the means to do so, but have we the will?
2.8 Conclusions There is a certain irony that, just when many national geological surveys are completing the stratigraphic mapping of their territories, when there is a greater awareness than ever of the requirement to meet diverse user needs and when the information technology revolution will enable the former to become easily available to the latter, the relevance of the geological survey as an active and dynamic service organisation is being questioned. It is almost as if the realisation that the rectangular-shaped, paper-based geological map, as we knew it, is an anachronism has made decision-makers believe that, maybe, the geological survey, too, is outdated. Nothing could be further from the truth. Increasing concern by the general public and by other non-geologists about the present and future state of our environment has provided a whole new range of users for geoscientific information. It has been argued here that, instead of the academic geologist being the most important user of geological outputs and the general public the least, the order should be reversed. As we move from the industrial to the information age and from an emphasis on mineral resources to one on the environment, so geologists must adjust to the new priorities. This new relevance would have come as no surprise to our predecessors in the lgthand early lgthcenturies. Geology is coming home!
Acknowledgements This paper has been strongly influenced by discussions over the years with many geologists and engineers in the public sector, in industry and in academia. In particular, I would like to thank John Cripps, Fred Bell, Dave McCann and Mlke Rosenbaum and colleagues in the Geohazards Group at the Nottingham Trent University. I am particularly indebted to June Page who has brought together the paper in its final form. I gratefully acknowledge the direct and indirect contributions that Keith Adlam, Alan Forster, Ian Jackson, Brian Kelk, Holger Kessler, Russell Lawley, Jo Mankelow, Ian Smith and Keith Westhead at the British Geological Survey have made to both the ideas and content of thls paper. Most of all, I want to thank the dedicated band of colleagues at the British Geological Survey who have kept the flag of engineering geology flying through thick and thin. This paper is published with the permission of the Director of the British Geological Survey (NERC).
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References Anon. (1972) The preparation of maps and plans in terms of engineering geology. Quarterly Journal of Engineering Geology, 5, 293-381 Anon. (1976) Engineering geological maps. A guide to their preparation. The UNESCO Press, Paris, 79pp. Anon. (1977) South east Essex geological and geotechnical survey. Open file report in 12 parts. Institute of Geological Sciences, London Anon (1981) Rock and soil description and classification for engineering geological mapping. Report by the International Association of Engineering Geology Commission on Engineering Geological Mapping. Bulletin of the International Association of Engineering Geology, 24, 235-274 Bush R (1974) The development of geological mapping in Britain between 1790 and 1825. Unpublished PhD thesis, University of London Coe L, Cratchley CR (1979) The influence of data point distribution on automatic contouring. Bulletin of the International Association of Engineering Geology, 19, 284-290 Conway BW, Forster A, Northmore KJ, Barclay WJ, Duncan SV (1980) South Wales Coalfield landslip survey. Institute of Geological Sciences Technical Report No. EG 8014 (2 volumes plus 20 maps) Conway Morris S (2000) Geology's Millennium Top 10. Earth Heritage, Jan 2000, 13-18 Craig GY (1984) John Clerk of Eldin (1728-1812) - artist and geological draftsman. In: Dudich, E. (ed), Contributions to the history of geological mapping. AkadCmiai Kiad6, Budapest, 383-389 Culshaw MG, Bell FG, Cripps JC (1988) Thematic geological mapping as an aid to engineering hazard avoidance in areas of abandoned mineworkings. In: MC Forde (ed), Construction in Areas of Abandoned Mineworkings. Proceedings of the 2nd International Conference, Edinburgh. Engineering Technics Press, Edinburgh, 69-76 Culshaw MG, Forster A, Cripps JC, Bell FG (1990) Applied geology maps for land-use planning in Great Britain. In: Proceedings of the 6" International Congress of the International Association of Engineering Geology, Amsterdam, August 1990. AA Balkema, Rotterdam, 1, 85-93 Forster A, Stewart M, Lawrence DJD, Arrick A, Cheney CS, Ward RS, Appleton JD, Highley DE, MacDonald AM, Roberts PD (1995) A geological foundation for planning and development in Wigan. Forster A, Arrick A, Culshaw MG, Johnston M (editors). British Geological Survey Technical Report, No. WNl9513 Friedman GM (2001) Birthplace of the geological sciences in America: New York State and the Erie Canal. Geoscientist, 11(1), 8-1 1 Glossop R (1969) Engineering geology and soil mechanics. Quarterly Journal of Engineering Geology, 2, 1-5 Johnston I (2000) The early development of modem geology. URL accessed: http:llwww.mala.bc.ca~-johnstoi/darwin/sect2.htm Langer W (1984) Early geological cartography in the Rhenish Slate Mountains, West Germany. In: Dudich E (ed), Contributions to the history of geological mapping. AkadCrniai Kiad6, Budapest, 83-92 Milanovsky EE (1984) Evolution of the geological map content relative to the development of geological science. In: Dudich E (ed), Contributions to the history of geological mapping. AkadCmiai Kiad6, Budapest, 3-8
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Owen T, Pilbeam E (1992) Ordnance Survey - map makers to Britain since 1791. Her Majesty's Stationery Office, London, 196 pp. Smith A, Ellison RA (1999) Applied geological maps for planning and development. A review of examples from England and Wales, 1983 to 1996. Quarterly Journal of Engineering Geology. Supplement to volume 32,44 pp. Tooley RV (1978) Maps and map-makers, 6thedition. BT Batsford, London, 140 pp. Terzaghi K (1948) Forward. GCotechnique, 1,3-5 Turner AK (2000) The impact of geology on society. Inaugural speech on the acceptance of the professorship in engineering geology, 15 March 2000. Published by the Technical University of Delft. 32 pp. Urban J (1984) On the development of geological mapping in Czeckoslovakia. In: Dudich E (ed), Contributions to the history of geological mapping. AkadCrniai Kiad6, Budapest, 103-117 Waters CN, Northmore KJ, Prince G, Bunton S, Butcher A, Highley DE, Lawrence DJD, Snee CPM (1996) A geological background for planning and development in the City of Bradford Metropolitan district. In: Waters CN, Northmore KJ, Prince G, Marker BR (editors). British Geological Survey Technical Report, No. WA/96/1 Wilson HE (1985) Down to Earth: one hundred and fifty years of the British Geological Survey. Scottish Academic Press, Edinburgh. 189 pp.
3 Definition of the modelling technologies A. Keith ~ u r n e r ' Delft University of Technology, Delft, The Netherlands also Colorado School of Mines, Golden, Colorado, USA
3.1 The Context Civil and geotechnical engineering and environmental applications depend on a precise definition of the shallow subsurface. Over the past decade, or slightly longer, a series of sophisticated three-dimensional modelling technologies have been developed to address this subsurface characterisation need. Currently available commercial modelling technologies reflect their historical origins. Further evolution and integration of these technologies within corporate decision-support systems are occurring rapidly, spurred by hardware and software developments, modern data base design concepts, and expanded information transmission capabilities across the Internet. The very shallow urban underground environment, where many infrastructure and utilities elements are located, presents very difficult characterisation problems. Characterising the deeper subsurface is important for groundwater resource evaluations, environmental contamination assessments, and for some infrastructure applications such as underground storage facilities.
3.2 Principles of geological characterisation The process of geological characterisation involves the determination of spatial variation, in the subsurface, of selected geological parameters. Petroleum exploration has for some time emphasized the use of seismic data, almost to the exclusion of alternative data sources. Other applications areas, including the mining, engineering, hydrogeology, and environmental restoration fields, already routinely use a broad suite of data sources. They also have a more integrated approach to threedimensional subsurface characterisation. Thus it is usual for samples and direct observations to provide only isolated data points. Spatial prediction and interpolation techniques must then be employed to determine the three-dimensional geometrical, or spatial, patterns of geological entities. Raper (1989; 1991) defined key concepts for subsurface characterisation and modelling - the ability to define and manipulate "geo-objects". Raper defined two types of geo-object. The first type, specified as a "sampling-limited geo-object", M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 27–40, 2003. c Springer-Verlag Berlin Heidelberg 2003
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could be any discrete physical entity, such as a rock layer or fault block. The ability to locate and manipulate such objects accurately in the subsurface largely depends upon the frequency of observation and sampling. The second type of geoobject, specified as a "definition-limited geo-object," represents a spatial volume defined by the distribution of a threshold parameter. A pollution plume, an ore body, or a sedimentary facies would be examples of such geo-objects. The ability to evaluate and manipulate such objects accurately in the subsurface largely depends upon having knowledge of the parameters controlling their definition. After a suitable geometrical representation of the geo-objects has been achieved, additional spatial interpolation techniques may then be used to identify discrete volumes containing these geo-objects and to develop appropriate spatial variation patterns describing properties within the geo-objects. Subsequently, such models can be used to provide volumetric distributions of measured attributes needed to conduct numerical processing andlor predictive modelling. Management alternatives can be evaluated, for example, once fluid migration pathways and volumes are determined, or once the distribution of geotechnical properties can be determined (D' Agnese et al., 1997). The volume and complexity of many datasets make it difficult to accomplish such integrated evaluations. Three-dimensional visualisation makes it easier to decipher trends and correlations. It allows geoscientists to explore multiple hypotheses and suggest new ideas regarding the nature of their data, often providing new insight and understanding. To date, three-dimensional subsurface characterisation and modelling has been generally restricted to one of the three following situations: geometrical representation of subsurface "geo-objects" visualisation of spatial heterogeneity estimated by simulation pre- and post-processing of data for use in numerical models. Unfortunately, such applications are each best served by different data representations. Because existing three-dimensional modelling systems are still evolving, it is not unusual for an application to require the use of multiple systems. However, data conversion between systems is usually difficult, thereby discouraging all but the most persistent users from fully using the available technologies.
3.3 Initial Developments The Experimental Cartography Unit (ECU) was established at the Royal College of Art in London in the early 1970's. The ECU experimented with the production of topographical, geological, and geochemical maps in co-operation with the then Institute of Geological Sciences (now the British Geological Survey, BGS) (Rhind, 1988). Production of their 150,000 scale map of Abingdon, UK, was thus probably the first standard series multi-colour geological map ever compiled using
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a computer. It also revealed the difficulties of matching geological and topographical features (Rhind, 1971). Such early efforts concentrated research on automating the cartographic process. Subsequently it was realized that computer technology could offer much more: the true power of computer technology lay in its ability to store, manipulate, and display spatial data. Geographers were greatly attracted by the abilities of the (then still crude) cartographic software to display their data, describing social, economic, and physical phenomena. Their efforts laid the groundwork for the evolution of Geographical Information Systems (GIs). Concurrently, the many disciplines involved in the rapidly evolving environmental movement were attracted to map overlay techniques offered by the cartographic computer-based software, for instance to identify areas where phenomena coincided and so defining suitability for, or sensitivity to, proposed activities. Landscape architects brought such an approach to a wider public, most notably in the book "Design With Nature" (McHarg, 1969). Although initially based on a manual process comprising the overlaying of map transparencies on light tables, computers and early GIs systems were soon used to carry out such tasks rapidly, and with precision. During this period there were no readily available commercial products to provide the generic tools needed for handling spatially distributed data (Rhind, 1981). This changed dramatically in the 1980's. Broad commercial acceptance of GIs had been achieved in North America by about 1988, and this initial acceptance rapidly expanded worldwide over the next few years. The term "Geographic Information System" (or "Geographical Information System", especially in Europe), along with its acronym "CIS", became widely adopted. However, geological exploration applications formed only a very small part of the global GIs market. Consequently, geological GIs applications had to adapt to a generic set of tools that only partly addressed their specific needs. In some cases, these needs could be readily satisfied, either by adapting the existing GIs tools, or by developing customized software modules. For example, specific software modules were developed to assist in the acceptance of common data formats, and symbol sets were provided to create traditional map displays.
3.4 Categories of GIs Driven by technological and economic considerations, the early commercial GIs products developed with two competing data structures, termed "raster" and "vector". Some early systems tended to support the raster data structure; others supported the vector data structure. Only later did the computer technologies allow for surviving commercial GIs systems to equally support both data structures. Raster CIS represent the study area with an array of small, uniform-sized cells, usually square or rectangular in shape. Each cell is coded to reflect the dominant value or class of the map feature being represented. Spatial relationships are readily derived from the positions of the cells within the matrix or array. In contrast, vector GIs represent map features as digitised points, lines, and polygon areas.
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Lines are defined by a sequence of digitised points, and polygonal areas by linking a series of lines forming their boundaries. A hierarchy of features and spatial relationships can thus be evaluated by using the topological relations among the point, line, and area features forming the mapped region. The vector GIs approach is especially good at representing sharp and unambiguous boundaries between map features. So vector GIs are preferred for displaying geological, land ownership, and facilities maps with high cartographic fidelity. The raster-based GIs are more often used during resource assessment and modelling. This is because the raster data structure: is readily linked to most numerical modelling functions provides adequate resolution for the creation of interpretive maps that often involve ambiguous or gradational boundaries between map features offers a convenient interface to many geophysical and satellite data sources, and allows for the discrete areas to be identified as well as display continuously varying phenomena.
3.5 Development of Geoscientific Information Systems By the late 1980's it had become apparent that the conventional GIs products, with their predominantly two-dimensional spatial viewpoint, could not entirely satisfy geological analysis requirements. In particular, detailed definition of subsurface conditions, rather than just portrayal of their areal extent of geological, hydrological, climate, or environmental features, required volumetric representation referenced to three orthogonal axes. Numerical modelling and assessment of subsurface conditions further required that these volumetric data be linked to three-dimensional data manipulation procedures. A number of research initiatives began to explore the possible extensions of conventional GIs to better serve the geoscience community. As far as can be determined, it was German geoscientists who first used the term "geoscientific information system" to describe the new approach (Vinken, 1986; 1988a; 1992a; 1992b). Initially they favoured the term "digital geoscientific map series" because they believed the terms "information system" did not fully describe the complexities of geological data capture, systemisation, and dissemination (Vinken, 1986; 1988b). At about the same time, Dutch geoscientists were concerned with developing comprehensive computer-based data handling and modelling applications for oil and gas exploration, and created the terms "GeoScientific Information System" with the acronym "GSIS" to describe their applications (Ritsema et al., 1988). Meanwhile, Raper (1989) and many British developers were referring to similar systems as "Geoscientific Mapping and Modelling Systems" with the acronym "GMMS".
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Since about 1989, the term "GeoScientific Information System" and the acronym "GSIS" have become more frequently used to describe computer-based threedimensional systems designed to handle the variety of data and analysis needs of the geoscience community (Turner, 1991a; Bonham-Carter, 1994). The terms "GeoScientific Information System" and "GSIS" appear preferable to the alternatives because they offer a parallel to the widely applied "Geographical Information System" and its well known acronym "GIs".
3.6 Capabilities of GeoScientific Information Systems The need for the acronym GSIS has been questioned, not least because many consider such geoscientific information systems as providing nothing more than support for standard GIs functions in a three-dimensional context. Some critics prefer to use the term "3D GIs". Yet the GSIS concept includes much more than just support for a three-dimensional geometry. When attempting to define, model, or visualize subsurface conditions, the geoscientist is usually faced with extremely sparse data, considerable complexity in subsurface conditions, and various types of discontinuities. Thus additional capabilities and functions must be provided within GSIS in order to fully support subsurface characterisation. In general, GSIS must include: rapid and complex data interpolation and extrapolation procedures provide for interactive data manipulation, and support rapid visualization. They must also support the range of distinctive data formats commonly encountered within a project, and should provide ways to illustrate the uncertainty inherent in the modelling process. No entirely satisfactory GSIS products yet exist that meet all these criteria.
3.7 Advances in hardware and software technology The transition from two-dimensional GIs to truly three-dimensional GSIS demands considerable increase in the complexity of the underlying data management, analysis, and display functions. The level of computational effort as one moves from GIs to GSIS does not increase linearly, say from "2x" to "3xW,but more or less exponentially, or by an order of magnitude, from say "x2" to "x3". Thus, although many scientists recognized the concepts and functions of GSIS in the mid 1980's, a practical GSIS implementation had to wait the arrival of sufficiently powerful and affordable computer hardware. The late 1980's saw several technological breakthroughs in computer technology that resulted in the production of a new class of high-performance graphical workstations. These workstations crossed a critical price-performance threshold.
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They provided graphical displays and numerical computation speeds that could, for the first time, support the three-dimensional subsurface characterization and modelling requirements required by the GSIS concept. Although the costs of these early graphical workstations were still quite high, petroleum company research laboratories and some government and university research facilities were able to procure limited numbers of these machines to undertake GSIS research and development. While the past decade has shown dramatic improvements in hardware, corresponding improvements have not occurred in GSIS software. All the major commercial GSIS products can trace their early development to the period in the late 1980's when the high-performance graphical workstations first became available. Since that time the various systems have matured, and in many cases have added desirable features. But their fundamental architecture has not evolved. For instance, they do not fully reflect the structures facilitated by current computer systems engineering. This results from economic realities - the geoscience market has not been able to grow sufficiently to be able compete with other application fields for the attention of the programming teams. The current commercial GSIS products are able to perform the necessary analysis functions, but they remain expensive and somewhat difficult to use, not least because their user interfaces appear outdated and data transfer is somewhat difficult. In contrast, a number of commercial GIs products, which are supported by strong user demands from a variety of fields, are rapidly evolving into "mainsteam" corporate, or "enterprise-wide", data management tools. Their costs have dropped while their analytical capabilities have greatly increased. It is now feasible, for instance, to undertake complex GIs analysis for a large project using relatively "standard" desktop personal computers. As these capabilities become integrated within corporate data management paradigms, the use of GIs can be expected to continue to grow. The situation for GSIS appears favourable for re-development of the software systems in a somewhat similar fashion to those observed for recent GIs developments. The future expansion of GSIS applications will depend on improvement of their user interfaces, increased integration with modern data base concepts, reduced software costs, and the ability to rapidly and easily exchange and disseminate 3D data structures. The long-term role of the Internet in the data exchange and dissemination of 3D subsurface data seems assured. Current capabilities are not entirely satisfactory, but several research projects are addressing this requirement.
3.8 Classes of Geoscientific Information System Three-dimensional GSIS technologies, although less mature than their twodimensional GIs counterparts, may also be divided into two major classes on the basis of their method for defining spatial volumes (Figure 3.1). Volume representation methods are the three-dimensional equivalent of the two-dimensional raster; they divide the volume into discrete "volume elements", or "voxels". Surface rep-
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resentation methods provide the three-dimensional equivalent of the vector-based GIs. The majority of GSIS available commercially utilize voxels. Voxel methods offer a data structure upon which more specialized applications can be built fairly rapidly. They thus offer commercial advantages. In addition, voxel techniques are extensively used in medical imaging, they provide opportunities for displaying continuously varying phenomena, and they are more easily related to the gridbased structure used by many numerical models of physical systems. However, voxel representations can require very large amounts of data storage. For example, a volume represented by 100 rows by 100 columns by 100 layers requires 1,000,000 voxels, and yet only provides a relatively low-resolution image. In practice, volumes represented by tens or hundreds of millions of cells are more likely to be desired. 3D SPATIAL REPRESEHfNlONS
REPRESEMATlON
SURFACE REPRESENTATIOH
VOXEL
Figure 3.1. Three-Dimensional Spatial Representations
Medical imaging applications commonly use data compression techniques involving octrees and their derivatives (Gargantini, 1991). Such methods have not proved as successful for geological applications; only a few applications have been reported (Kavouras, 1987; Bak and Mill, 1989; Samet, 1989; 1990a; 1990b).
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Figure 3.2. A Geocellular model showing multiple stratigraphic objects
Figure 3.3. Salt diapir (with tone-filled elevation contours) simulation with sand layers [Image courtesy of Dynamic Graphics, Inc.]
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Because many geological features are relatively thin and widespread, they are more continuous in the horizontal (x- and y-axis) directions, and show much more variability in the vertical (z-axis) direction. Some commercial GSIS products (e.g. Stratamodel) reflect this characteristic by allowing partially deformable "geocellular" voxels (Denver and Phillips, 1990). These have uniform dimensions in the xand y-directions and variable dimensions in the z-direction (Figure 3.2). Other commercial GSIS products (e.g. Earthvision) convert a voxel data structure into a three-dimensional isosurface prior to display, as illustrated in Figure 3.3 (Smith and Paradis, 1989; Belcher and Paradis, 1991). Surface representation methods have been less widely used in commercial GSIS. They involve a variety of techniques that are the equivalent of twodimensional vector GIs methods. Constructive solid geometry methods allow the development of complex shapes by combining simple geometrical primitives, including cylinders, cones, and parallelepipeds (Bak and Mill, 1989; Kavouras, 1991). These methods are often used to describe man-made features, such as excavations, but are not suitable for representing naturally occurring geological features (Houlding, 1994). Boundary representation methods include relatively simple polygon meshes and piecewise linear interpolations to define surfaces and geological features (Mallet, 1988; 1991; Houlding, 1991). These systems are frequently employed in commercial GSIS products used for mining applications (Houlding, 1994,2000). More recently, some systems have utilized unstructured meshes, the threedimensional equivalent of the triangulated irregular network method (commonly referred to as "TIN) used by vector GIs. Unstructured meshes may involve a variety of fundamental elements, including tetrahedrons, hexahedrons, and dodecahedrons, and differ from the simpler structured meshes (such as voxel systems) in that they are not constrained by having to have a constant node and face structure (Gable et al., 1996). This provides added flexibility during the development phase for a model that reflects subsurface conditions, but this flexibility comes at a price: added computational demands and very slow model construction unless sophisticated "mesh builder" software is developed and employed. Unstructured meshes also have the advantage of being able to link with finite element models. For these and other reasons, a number of research teams have invested considerable effort in developing unstructured 3D mesh systems, and "mesh builders". Fisher and Wales (1990; 1992), Kelk and Challen (1992), and Lasseter (1992) have described experimental applications of curved surface and solid modelling based on advanced mathematical spline functions. For technical reasons, the most appropriate of these spline-fitting approaches appears to be the "Non-Uniform Rationale B-Spline", or "NURBS" (Figure 3 S).
3.9 Towards a desirable 3D data structure Data management, correlation, and integration form the basis for geological characterisation. A variety of data types, data structures, and data management func-
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tionality are necessary for efficient 3D, qualitative, quantitative, and visual representation of the geological subsurface. Geoscience data types include characteristics (lithology, mineralogy, classifiers and codes), variables (concentration, grade, contaminants and properties), and 3D co-ordinates. Co-ordinates can reflect a variety of location systems ranging from global, such as Universal Transverse Mercator (UTM), to regional orthogonal systems containing northing, easting, and elevation values (NEL), or even the XYZ values of a local system. Efficient organization of large amounts of data is vital to a successful subsurface characterisation. Therefore, efficient data management issues must be addressed. Houlding (1994) suggests that 3D subsurface characterisation requires four data structures: hole data, map data, volume data, and 3D grid data. Each of these data structures employs a different 3D geometrical definition. Hole and map data structures provide vehicles for managing information derived from site investigation. For many projects, the majority of information is accumulated using boreholes. The identifiers for a borehole data structure would include borehole name, region, and description of the type of borehole data.
Figure 3.4. An unstructured 3D mesh system to portray an alluvial sequence
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Figure 3.5. 3D NURBS model of two alluvial channels buried in South Platte River sediments. The channels were interpreted from cross-sections (shown as lines on surfaces in image). Cross sections were developed from borings (Turner, 1991b)
The geometry and locators of the borehole data structure include interval distance, collar or starting elevationko-ordinates and survey records. Furthermore, the characteristic data source would include lithological and mineralogical intersections, rock properties and, if available, borehole geophysics traces. Map data structures can similarly be identified, starting with a map name and description. The map data locators would be the co-ordinates defining the map's origin, and the azimuth and dip of its plane. The geometry of features would be portrayed as points, lines and polygons. Geological maps, cross-sections, site plans and surveys, contours, point samples, and surface geophysics would be common data sources. Surface and volume data structures are used to interpret and predict structural and stratigraphic conditions, and for representing geotechnical engineering design information. Triangulated meshes utilising the TIN structure can efficiently represent surfaces. In layered sedimentary structures, a volume model can be readily derived from surfaces. The component geometry of a 3D model is obtained by subtracting the lower TIN elevation values from the upper TIN elevation values. In more complex geological structures the volume geometry is more easily constructed using closed polygonal boundaries. One method uses three parallel planes - a mid-plane and two parallel planes on either side of the mid-plane. These outer planes are referred to as the fore-plane and back-plane. Connecting these planes by straight lines linking the corresponding points in each plane creates a volume using boundary representation methods. A 3D-grid data structure allows prediction of continuous measures of spatial variation. Values for the desired variable are calculated and attributed to the centre
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of each grid cell. The 3D-grid data structure representation has an origin, orientation, and dimensions.
3.10 The geological characterisation process The various GSIS products approach the geological characterisation process in somewhat different ways. These differences largely reflect the typical geological environments that each system was developed for - some were developed for sedimentary environments while others were developed for hard-rock minerals extraction. In spite of such differences in approach, the geological characterisation process can be broken down into the following steps (Houlding, 1994): Data management and integration of acquired information for a particular study area. Spatial correlation of data is an integral part of this step. Data validation in terms of quality control and evaluation. Evaluation of the data allows determination of the the size and complexity of the project. Interpretation of geological parameters. From the available data, the geological structure and stratigraphy of the area needs to be interpreted in order to properly characterize the subsurface. Prediction of spatial variability of variables or properties. Spatial prediction techniques, such as Inverse Distance Squared or Kriging (Houlding, 1994; 2000), may be applied to the various sample values to estimate the spatial variation. Along with these spatial predictions, information can be obtained concerning the uncertainties of the variable distributions. With the data created from the interpretation and prediction processes (steps 3 and 4 above), a final evaluation may be made and the subsurface characterization model then used to undertake evaluations of the study area.
References Bak PRG, Mill AJB (1989) Three dimensional representation in a geoscientific resource management system for the minerals industry. In JF Raper (ed), Three dimensional applications in geographcal information systems, Taylor and Francis, London, 155-182 Belcher RC, Paradis AR (1991) A mapping approach to three-dmensional modeling: in Three-Dimensional Modeling with Geoscientific Information Systems. In: AK Turner (ed), NATO AS1 Series C: Mathematical and Physical Sciences, 354, Kluwer Academic Publishers, Dordrecht, The Netherlands, 107-122 Bonharn-Carter GF (1994) Geographic Information Systems for Geoscientists - Modelling with GIs: Pergamon/ElsevierScience Publications, 398 pp. D'Agnese FA, Faunt CC, Turner AK, Hill MC (1997) Hydrogeologic evaluation and numerical simulation of the Death Valley regional ground-water flow system, Nevada
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and California: U.S. Geological Survey Water Resources Investigations Report 964300,124 pp. Denver LF, Phillips DC (1990) Stratigraphic geocellular modeling: Geobyte, 5(1), 45-47 Fisher TR, Wales RQ (1990) 3D solid modeling of sandstone reservoirs using NURBS: Geobyte, 5(1), 39-41 Fisher TR, Wales RQ (1992) Rational splines and multi-dimensional geologic modelling: No. 41, Lecture notes in Earth Sciences, Springer-Verlag, Heidelberg-Berlin, 17-28 Gable CW, Trease HE, Cherry TA (1996) Automated Grid Generation From Models of Complex Geologic Structure and Stratigraphy, Los Alamos National Laboratory Report LA-UR-96-1083, Los Alamos, New Mexico Gargantini I (1991) Modeling natural objects via octrees. In AK Turner (ed) ThreeDimensional Modeling with Geoscientific Information Systems, NATO AS1 Series C: Mathematical and Physical Sciences, 354, Kluwer Academic Publishers, Dordrecht, The Netherlands, 145-158 HoulQng SW (1991) The application of new 3D computer modelling techniques to mining: inThree-Dimensional Modeling with Geoscientific Information Systems. In AK Turner (ed) NATO AS1 Series C: Mathematical and Physical Sciences, 354, Kluwer Academic Publishers, Dordrecht, The Netherlands, 303-325 Houlding SW (1994) 3D Geoscience Modeling - Computer Techniques for Geological Characterization: Springer-Verlag Berlin-Heidelberg, 309 pp. Houlding SW (2000) Practical Geostatistics - Modeling and Spatial Analysis: SpringerVerlag Berlin-Heidelberg, 160 pp. Kavouras M (1987) A spatial information system for the geosciences: PhD Dissertation, University of New Brunswick, Fredericton, New Brunswick, Canada Kavouras M (1991) A spatial information system with advanced modeling capabilities. In AK Turner (ed) Three-Dimensional Modeling with Geoscientific Information Systems, NATO AS1 Series C: Mathematical and Physical Sciences, 354, Kluwer Academic Publishers, Dordrecht, The Netherlands, 59-67 Kelk B, Challen K (1992) Experiments with a CAD system for spatial modelling of geoscientific data: in From geoscientific map series to geo-information systems: Proceedings Second International Colloquium, Wurzburg, Germany, September 9- 11, 1989; Bundesanstalt fur Geowissenschaften und Rohstoffe, Hannover, Geologisches Jahrbuch, Reihe A, Heft 122,145-153 Lasseter TJ (1992) An interactive 3D modelling system for integrated interpretation in hydrocarbon reservoir exploration and production: No. 41, Lecture notes in Earth Sciences, Springer-Verlag, Heidelberg-Berlin, 189-198 Mallet JL (1988) Three-dimensional geographic display of disconnected bodies. Mathematical Geology, 20(8), 977-990 Mallet JL (1991) GOCAD: a computer aided design program for geological applications. In AK Turner (ed) Three-Dimensional Modeling with Geoscientific Information Systems, NATO AS1 Series C: Mathematical and Physical Sciences, 354, Kluwer Academic Publishers, Dordrecht, The Netherlands, 123-142 McHarg IL (1969) Design With Nature. Natural History Press, New York, 197 p. Raper JF (1989) The 3D geoscientific mapping and modelling system: a conceptual design: In: JF Raper (ed) Three-dimensional applications in geographic information systems, Taylor and Francis, London, 11-19 Raper JF (1991) Key 3D modelling concepts for geoscientific analysis. In: AK Turner (ed) Three-Dimensional Modeling with Geoscientific Information Systems, NATO AS1 Se-
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ries C: Mathematical and Physical Sciences, 354, Kluwer Academic Publishers, Dordrecht, The Netherlands, 215-232 Rhind DW (1971) The production of a multi-colour geological map by automated means. Nachr. Aus den Karten und Vermessungeswesen, Heft 52,47-52 Rhind DW (1981) Geographical information systems in Britain. In: Wrigley, N, Bennett RJ (eds) Quantitative Geography, Routledge and Kegan Paul, London, 17-39 Rhind DW (1988) Personality as a factor in the development of a discipline - the example of computer-assisted cartography. American Geographer, 15(3), 277-289 Ritsema IL, Riepen M, Ridder J, Paige SL (1988) Global system definition for version 0.x of the GeoScientific Information System. SWS Document GSIS 1.1, TNO Institute of Applied Geoscience, Delft, The Netherlands Samet H (1989) Applications of spatial data structures - computer graphics, image processing, and GIs: Addison-Wesley, Reading, MA, 507 pp. Samet H (1990a) The design and analysis of spatial data structures. Addison-Wesley, Reading, MA Samet H (1990b) Applications of spatial data structures. Addison-Wesley, Reading, MA Smith DR, Paradis AR (1989) Three-dimensional GIs for the earth sciences. In: JF Raper (ed) Three-Dimensional Applications in Geographic Information Systems, Taylor and Francis, London, 149-1 54 Turner AK (Editor) (199 1a) Three-dimensional modeling with geoscientific information systems. NATO AS1 Series C:Mathematical and Physical Sciences, 354, Kluwer Academic Publishers, Dordrecht, The Netherlands, 443 pp. Turner AK (199 1b) Applications of three-dimensional geoscientific mapping and modelling systems to hydrogeological studies. In: AK Turner (ed) Three-dimensional modeling with geoscientific information systems: NATO AS1 Series C:Mathematical and Physical Sciences, 354, Kluwer Academic Publishers, Dordrecht, The Netherlands, Chapter 21,327-364 Vinken R (1986) Digital geoscientific maps: A priority program of the German Society for the Advancement of Scientific Research. Mathematical Geology, 18,237-246 Vinken R (Compiler) (1988a) Construction and display of geoscientific maps derived from databases. Proceedings Intl. colloquium, Dinkelsbuhl, Germany, December 2-4, 1986; Bundesanstalt fur Geowissenschaften und Rohstoffe, Hannover, Geologisches Jahrbuch, Reihe A, Heft 104,475 pp. Vinken R (1988b) Digital geoscientific maps - A research project of the Deutsche Forschungsgemeinschaft (German Research Foundation). In R Vinken (ed) Construction and display of geoscientific maps derived from databases. Proceedings Intl. Colloquium, Dinkelsbuhl, Germany, December 2-4, 1986, Bundesanstalt fur Geowissenschaften und Rohstoffe, Hannover, Geologisches Jahrbuch, Reihe A, Heft 104, 7-20 Vinken R (Compiler) (1992a) From geoscientific map series to geo-information systems. Proceedings Second Internationa. Colloquium, Wurzburg, Germany, September 9-1 1, 1989; Bundesanstalt fur Geowissenschaften und Rohstoffe, Hannover, Geologisches Jahrbuch, Reihe A, Heft 122,501 pp. Vinken R (1992b) From digital map series in the geosciences to a geo-information system. In From geoscientific map series to geo-information systems: Proceedings Second Intl. Colloquium, Wurzburg, Germany, September 9-11, 1989; Bundesanstalt fur Geowissenschaften und Rohstoffe, Hannover, Geologisches Jahrbuch, Reihe A, Heft 122, 725
4 New paradigms for geoscience information management Khalil H. ~ a s s e r 'Stephen , ~ o l i v a rJulie ~ , canepa2,Alison ~ o r r i e s ' revision, Inc., Denver, Colorado, USA LOSAlamos National Laboratory, Risk Reduction and Environmental Stewardship Division, Los Alamos, New Mexico, USA
4.1 Introduction Over the past two decades, geoscientists increasingly have been engaged in providing answers to complex environmental problems that have significant societal, political, and economic consequences. Today's geoscientific community faces greater public, governmental, and regulatory accountability. Clearly, a new information management (IM) paradigm must be established to better meet client and stakeholder needs. The systems used to store and generate geoscientific data must be re-engineered to meet those needs. In order to effectively respond to societal requirements, geoscientists are now faced with the challenge of cost effectively collecting, managing, analysing, and synthesising multidisciplinary complex information. This data must be processed and disseminated in such a way that allows the public to make informed and rational decisions. This paper addresses the challenges and the implications for geosciences information management in the 21" century. It concludes with a case study for a successful implementation of the new paradigms in an Environmental Restoration Project at the Los Alamos National Laboratory (LANL) by the Department of Energy (DOE). The project saw a need to upgrade and re-engineer its data and business processes to more adequately address client, user, and stakeholder issues of data accessibility, control, and quality.
4.2 Challenges facing the geoscience community Risk management decisions have significant potential environmental, political, and economic consequences. These decisions are reached using large sets of multidisciplinary data that require timely and thorough reporting. In this situation, the geoscientific community faces specific challenges as it learns how to function under the following conditions. M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 41–58, 2003. c Springer-Verlag Berlin Heidelberg 2003
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First, environmental work involves extensive and expensive work processes that must follow numerous federal, state, and local regulations. Stakeholders may not understand or accept highly technical explanations for remediation methodologies or procedures, yet all of these must be well documented. Addressing multiple-decision scenarios requires additional time and expense. All these situations frustrate geoscientists who typically are more interested in conducting good scientific work than in creating paper trails. Second, effective tools for disseminating complex scientific concepts are not always available to the geoscientific community, or the community is not always familiar with those tools that are available. If highly technical information is not presented well to a non-technical audience, the non-technical stakeholder may interpret the presentation as a cover-up, an attempt to hide greater problems than actually exist. The response may be a demand for more sampling and data-gathering that does not clarify technical conditions but does increase costs (and frustration levels!) for the public and geoscientists alike. Finally, most geoscientists are not involved in the legal aspects of defending data and remediation decisions. Consequently, they may not generate an audit trail that would allow rebuilding or defending such decisions. Issues of compatibility and integration of work processes and technologies also challenge the geoscientific community. At a corporate level, poor communication and information exchange between corporate groups results in redundant activities and conflicting results. For example, when different organisational units conduct the same analyses without knowing of each other's activities, the result is duplicate analytical effort; multiple storage, format and locations (e.g. spreadsheets and desktop databases) instead of a common data warehouse; and unwanted cost. In addition, inconsistent operating procedures and the use of different and/or incompatible equipment and software creates a difficult environment for integrating data and interpreting results. At the group level, daily tasks are impacted if highly specialised applications (i.e. customised to the group's internal work processes) limit effective communication and information exchange between units. Updating information and establishing and documenting audit trails becomes time-consuming, expensive, and difficult. At the corporate level, complimentary supporting studies often are not conducted interactively because inconsistent processes impose technically difficult burdens on the personnel. For example, inconsistent data collection, data analysis, and quality assessment methodologies can make comparing similar data very difficult, and unnecessarily increases the risk of incorrectly interpreting results. This situation hinders the integration of multidisciplinary work groups into an effective decision-making process. Understandably, such situations are less than optimal in the light of recent stakeholder and economic demands. This is because they make group-level deliveries unacceptably high in terms of time, cost, and quality. The lack of formal processes for data control and data quality, software standards, and protocols can threaten an organisation's credibility and weaken defensibility of results and decision-making.
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4.3 The need for new paradigms Clients and stakeholders have specific environmental and societal concerns. They want cost-effective answers to their questions, real-time access to data and an audit trail that ensures sound data control and data quality procedures have been followed (D'Agnese and O'Brien, this volume). To meet these concerns, the geoscientific community must shift to a new geoscientific Information Management (IM) paradigm that examines and realigns organisations, co-ordinates and formalises work processes, and integrates technologies. These efforts must support realtime data access and ensure that data control and data quality processes are established. 4.3.1 The challenge
Typically, organisations provide a range of services and products to meet the demands of customers and stakeholders. Successful corporations can be characterised as organisations of people that use a set of work-processes and technologies with the goal of satisfying the customer inside a specific financial perspective (Figure 4.1). Customers want products that meet their time, cost, and quality needs. An organisation's challenge is to align itself (i.e. create a pragmatic collection of people) so as to integrate practical work processes (i.e. re-engineer existing work processes), using appropriate technology (such as an enterprise system) to ensure that quality products are delivered on time at competitive costs. This people-processes-technology relationship is discussed further below. 4.3.2 Organisational alignment to meet stakeholderlclient needs
Traditionally, most organisations within the geoscientific community are well suited to function at the unit level. For example, a typical geoscientific organisation might include a geology group, a hydrogeology group, and a geochemistry group. These people, processes, and technologies focus their expertise to meet the goals of a specific group, which often is driven by research and development (R&D). Their focus is science, not risk-management decision-making. They may be organisationally unsuited to providing the kind of risk-management decisions today's stakeholders and clients expect. However, a client may need only a cursory understanding of the geology, hydrogeology, or geochemical inventory of a site. In today's world, the client or stakeholder may be more interested in environmental liability and in supporting risk-reduction than in the R&D aspects of an environmental problem. In this case, there is an obvious gap between the organisation's goals (fact-gathering and processing in an R&D setting) and the client's expectations (decision-support information). Therefore, closing this gap and providing satisfactory solutions for the stakeholders and clients becomes a priority. A shift is needed to a customer-centred
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work mode that focuses on generating comprehensive results, multiple R&D groups, and translating the results into a language that stakeholders understand.
Q Customer
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Figure 4.1. Paradigm of a 'Pragmatic and Practical' organisation
4.3.3 Re-engineering the work process Existing work processes can be re-engineered to meet stakeholder and client needs. According to D'Agnese and O'Brien (2002), today's geoscientists must learn to accept re-engineered processes and new technology that in turn will better meet customer requirements. Re-engineering work processes are challenging, completely transforming the organisation's character. Re-engineering often replaces complex, tedious processes with simple, integrated work processes. Figure 4.2 shows a complex work process. Figure 4.3 shows a re-engineered work process. Successfid re-engineering focuses on developing specific metrics to ensure products will meet the customer's quality, cost, and schedule requirements (Darnton and Darnton, 1997). Organisations tend to behave according to established metrics. For example, if the metric is to produce 1000 lines of computer programming code a day, most employees will focus on producing computer code quickly rather than producing quality computer programs. The corporation, in this case, risks producing low-quality products that may meet the customer's schedule and cost requirements but are most unlikely to meet quality needs. If the metric is to produce "bug-free" computer programs, most employees will focus on producing quality computer programs with less attention being paid to production sched-
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ule and cost. The corporation, in this case, risks producing high-quality computer programs that may not meet the customer's schedule or cost requirements. During the re-engineering process, organisations must establish their goals (metrics) with respect their customer's requirements. Technology adds value to an organisation when it tracks and monitors those metrics to support continued enhancement of work processes (Information Delivery Incorporated, 2002). That is, if the stakeholder is interested in quality data, metrics must be created for applicable work processes, data control, and data quality. 4.3.4 An implementation strategy
To create a successful new IM paradigm, organisations must evaluate their existing processes and develop a solid implementation strategy to realign current work models in the light of new stakeholder and client demands. Re-engineering efforts should incorporate new technologies as old processes change. The challenges can be summed up in the following ways. Work processes and their associated metrics must be defined before new technology is implemented New technology must be developed in accordance with corporate and regulatory requirements and needs The technology must be designed and implemented cost-effectively The technology must be understood and accepted by the organisation and its employees (users) The resultant product must satisfy the needs of stakeholder and clients. If the new paradigm is to be an improvement, it needs to be recognised that costeffectiveness is not synonymous with lowest price. Cost-effectiveness is a function of the quality and robustness of the current technology combined with organisational acceptance. For example, even the most robust system, delivered at a low price, may not be cost-effective if it is not used (or worse, is used inappropriately) because it is perceived as being "no good" or not capable of being fitted into the user's daily workflow. On the other hand, even if a system is developed at a reasonable cost and is accepted by the organisation, it may be not be cost-effective if it has high maintenance costs or long downtimes. Commitment to a well-developed implementation plan is key to introducing new technologies that change work processes. A successful new plan considers many issues: financial limitations, human-resources, time constraints, development and implementation factors that address how to work in the old system while the new system is being built and, most importantly, cost-effective technologies based on customer needs.
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Figure 4.2. Example of an 'As-Is' work process
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Figure 4.3. Example of a 'To-Be' work process
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The ER Project is responsible for the characterisation and clean up of 2124 potential release sites (PRS). These PRSs resulted predominantly from weapons R&D activities conducted since 1942. To meet ER Project goals and deliverables, the project conducts field-sampling activities to determine possible contaminants as well as the levels and geographic extent of contamination. These sampling activities generate significant amounts of geological, hydrogeological, geochemical, and contaminant information. Ensuring accurate environmental appreciation, data quality review, analysis, and evaluation are integral to the project's business processes. A priority for project management was the creation of an infrastructure that would provide cost-effective data management and associated activities such as verification and validation. This infrastructure ultimately will support a defensible environmental administrative record that is generated and maintained with standard extraction, visualisation, reporting, and information-dissemination tools, and integrated robust tracking and control tools. 4.4.1 ER Project goals and needs
In addition to supporting the administrative record, ER Project management is committed to streamlining existing work processes that support quality environmental decision-analysis. One goal of this process improvement is to better meet the needs of the non-ER Project stakeholders with respect to the environmental decision issues facing LANL and surrounding communities. A second goal is to support the requirements and performance standards established by LANL, ER Project management, and state and federal regulators. Project management determined that improving decision analysis was dependent on improving three factors: data availability, data control, and data quality. To accomplish these goals, management-level needs were assessed. Extensive interviewing identified a need for the following: 8
a strategy to implement an IM system a qualified IM team to direct IM and re-engineering activities an effective IM infrastructure for information storage and retrieval formal processes to track information flow robust information audit trail processes more effective data-sharing with stakeholders.
4.4.2 Business process analysis
The next step in meeting improvement goals and fulfilling project needs was a business process analysis (BPA). A BPA consists of compiling an inventory and mapping existing work processes and data flow throughout an organisation. It identifies the people (roles and responsibilities), data, processes (how data moves through the organisation), and tools (manual and automated) that allow an organisation to perform its work. The BPA identifies gaps, inefficiencies, and current
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misalignment with stated goals. The BPA then presents opportunities for reengineering these existing work processes. The ER Project BPA determined that the following factors prevented the organisation from fully realising its goals. People
o
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Data quality and control functions were spread throughout the organisation, making effective management difficult. There was no centralised IM leadership.
Data
Data were scattered across the organisation in multiple, sometimes incompatible, formats. Little technical, regulatory, global indexing system (GIs) and management data were integrated. o Collection and reporting standards were not always followed. o The data-collection process, from initial generation to final deposit in a document repository, needed improvement. o Better documentation of data quality and integrity was needed. Processes o Sample management processes were inefficient. o Data management processes were not integrated with other work processes. o Document management processes were not integrated with sample and data management processes. Tools o Current tools for data management activities were insufficient and did not effectively support each other. o
4.4.3 Success factors An IM team assessed the results of the BPA and extracted several critical success factors. While some factors may be more important than others, typically all are necessary for a successful outcome. The project had to address the following: development of a management system plan creation of an IM technical team re-engineering of business processes hardwarelsoftware infrastructure implementation conceptual design development database design and GIs integration data migration and cleanup software definition (what the software will do) and development.
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4.4.4 Strategic plan development The next step was to develop an IM plan that addressed the needs and goals of the project, stakeholders and regulators. The critical factors dictated the sequence of events that needed to be accomplished. Other issues, described below, also had to be integrated. For example, the plan had to create a robust data repository that could easily accept information, from initial sample collection to final reporting. The repository had to hold not only standard data such as geochemical results, document links, photographs, borehole logs, and the like, but also provide management with tracking information. Data needs to be added to the repository in a timely manner, for instance by allowing time for data quality reviews and facilitating responses to requests from the regulator for corrective action. Work processes had to be refined to ensure that analytical data from the laboratories reported in hard copy form was consistent with the electronic versions provided to the sample management group. Standard, user-friendly data-extraction tools were needed so that the data could be published. The new system had to capture documented data integrity in order to preserve the quality of the data sufficient to support decision analysis. Complete document control was needed to support the decision-making process. An IM strategic plan was developed and integrated into the overall ER Project strategic plan (Canepa, 1999), which was reviewed and approved by ER Project management and DOE. Their support was critical, because large-scale reengineering efforts rarely succeed unless there is strong management support and commitment. The IM strategic plan was developed using a phased approach; the new system had to work before the old system could be discarded. System components, new processes, and new roles and responsibilities were phased in so as to have a positive impact on the existing production processes. The team identified the following factors within the strategic plan as being critical to its success: the creation of an IM technical team, the re-engineering work processes, the infrastructure design and its implementation, the development of the conceptual design, creating a technical architecture for the database, developing a plan to clean up archival (legacy) data, and developing new software applications.
4.4.5 Creating the IM team IM resources were evaluated, an inventory develped, and centralised into a single team under a leadership that reported directly to the ER Project management. This team was responsible for developing and maintaining the IM system, meeting the project's technical and business requirements. Once the ER Project management had approved the IM strategic plan, the ER project made the commitment to create a centralised IM system that would support the transition from the old processes to the new. Both systems were to be maintained until the new processes were established, tested, and demonstrated to be functioning robustly. A major reorganisation was initiated to ensure that all IM
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groups were organised under one person and ensure that the mission was welldefined. Before the re-engineering could be initiated, the decentralised IM structure needed to allow each functional group to maintain and control its own data. The decentralised structure made many IM functions redundant: development efforts were hindered, duplicate data had to be stored and maintained in numerous places, and staff skills and training could not be not co-ordinated. This structure made coordinating efforts difficult and prevented development of an integrated system that could meet user, stakeholder, and regulatory needs.
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Legend Recn&ccrcd Process
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Figure 4.4. Example data flow diagram for a management business process.
4.4.6 Re-engineering the business processes The ER Project work processes could be categorised as either technical processes or business processes. These processes were then duly identified, an inventory compiled, and priorities set in accordance with the project's strategic plan. The strategy for process re-engineering focused on those processes which were critical to the project's short-term mission and to the planned longer-term system components. Business processes were integrated throughout the development phase and standard operating procedures (SOPS) updated, guidelines prepared, and quality assurance procedures established (Figure 4.4).
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4.4.7 Implementation of the hardwarelsoftware infrastructure
In order to more effectively meet stakeholder and ER Project needs, the IM strategic plan required the creation of an enterprise-wide system that allowed access to tools and data, and encouraged communication across the organisation. It also called forelimination of independent data holdings by individual groups and the use of incompatible technologies (Canepa, 1999). To achieve this, the hardware and software infrastructure had to be substantially upgraded and standardised. The infrastructure consisted of multiple hardware platforms. These included both Macintosh and Windows-based desktop and laptop computers, and Unixbased workstations. Users ran various incompatible spreadsheets and database management systems, including 4D, Access, SQL Server, and Oracle, and employed both Microsoft Internet Explorer and Netscape browsers. The infrastructure was also characterised by multiple domains, data was stored on desktops but not captured by the project's main database. Some users did not follow standard business practices: there was inadequate backup of files and protection against fire andlor theft. In short, although everything worked on a daily basis, data accessibility was not optimal, development efforts were impaired, and data processes were not well-documented. The ER Project concluded that although the multiple platforms and the legacy of archival systems could be accommodated, the cost of integrating and maintaining these systems outweighed the cost of upgrading with the development of a single integrated system. State-of-the-art technology was therefore implemented in order to create a single domain with a uniform, compatible hardware platform. Software options were standardised using centralised file and data servers. Daily backups that effected system redundancies were implemented so as to ensure reliable system operation. 4.4.8 Conceptual system design
Figure 4.5 shows a summary of the technical processes required to support the ER Project's goal of closing PRSs. The process automation application required for each of these work processes is also shown on this figure. In order to support the automation of the re-engineered processes and workflows, a conceptual design for the IM management system was developed, as shown on Figure 4.6. This system consists of a central database repository to store point data in a Relational Database Management System (RDBMS); a Spatial Database Engine (SDE) is used to store the geological data. A series of components were thus designed, falling into two categories: Data gathering applications Data visualisation and decision management tools. Once the conceptual design had been approved, the data gathering applications, the central repository, and the enterprise applications could be put into place. Figure 4.7 shows the architecture and technologies that were used.
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Figure 4.5. Work Process and Corresponding Automation Application
Figure 4.6. System Conceptual Design
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Data Warehouse Solutions 0 Custom Programming 0 XML I828 Solutions
Data Gathering
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SQL Server
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Figure 4.7. System Architecture and Technologies 4.4.9 Database design
The ER Project Database was designed to support all phases of the ER process, from start to end. It is a comprehensive design that allows the integration of workflow data and technical data within a single data store (the ERDB). The workflow data comprises the ER project business data, management data, and business rules which support the ER workflow process. The technical data comprises field data, results and analytical data, as well as the geospatial information necessary for geoscientific analysis. The database consists of 300 tables. These have complex relationships but with the potential to form a DOE environmental data management standard. Figure 4.8 shows the various database modules for the ER Project database. 4.4.10 Data migration and cleanup
Having deployed the ER database, the next step was to clean up the archival (legacy) data and transfer it to the new database structure. A significant amount of environmental data had been collected over many years, under a number of different regulators, and employing a variety of standards and methodologies. These data were stored in a wide variety of databases, and as hard copy. Data needed to move to the newly standardised structure and checked for accuracy, for instance against the hard copy. The data migration and cleanup included approximately 2.5 Million records of analytical data!
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One would think that any two systems that maintained similar data would have been performing similar tasks. Thus information from one system should map to another with relative ease. However, this hardly ever happened in practise! The archival systems proved to be far too lenient with respect to enforcing integrity at the elemental level of data. Fields that should have been populated from a list of valid values, such as STATES, tended to require that a specific value be entered, but these were seldom validated against the value intended by the user.
Figure 4.8. ER Project Database Modules
To prepare for and execute a successful data migration, it was recognised that data migration would need to comprise more than the simple act of transferring data from one system to another. Several questions had to be answered early on: Which were the source systems containing the archival data? What volume of data needed to migrate? How many interfaces would be involved? How much data cleansing would be required? This involved examining the actual data that had to be transferred. The data transfer plan was broken down into several phases: Strategic Phase Analytical Phase Implementation Phase The focus of the Strategic Phase was to determine the scope of the migration. In other words, answer the question, 'What we are trying to transfer?' This was the time to identify the number of archival systems requiring migration and establish their data structures. Interfaces are another critical factor that were identified at
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this juncture. Interfaces are no different from other data sources, except they may receive data from the new system, as well as supply data. At this point, the team was not only identifying the number of data files but also the number of different systems from which data were to be transferred. Data sources were not limited to actual data processing systems. Inevitably, employees were encountered that maintained files on their own workstations, used to accomplish specific tasks essentially for their own use. The Analytical Phase was not intended to thoroughly identify the transformation rules by which historical data would be massaged into the new system; rather, it involved making a checklist of the archival data elements that had to be transferred. This list of data elements came from the following sources: Archival data analysis. Comprehensive data cleansing research was undertaken, extending the information into the Strategic Phase. Archival report audits. Archival reports from the old system were reviewed. A comprehensive list of field usage was thereby generated. User feedback sessions. The Implementation phase is where the bulk of the actual mapping of archival data elements to columns took place. Note: Data migration is iterative - it cannot happen in a single sitting. The mapping portion of a data migration project spanned the Analytical Phase across to the Implementation Phase. The reason for this is that the most important resources for validating the migration are the users of the new system. Unfortunately, they are unlikely to be able to grasp the comprehensiveness of the migration until they can view the data through the new applications. Experience leads to the conclusion that developing the new reports prior to invoking the new forms permits a more thorough validation of the migration, earlier in the project. For instance, if some sort of calculation was performed incorrectly by a migration script, reports would reflect this. A form typically displays a single master record at a time, whereas reports can display several records on each page, making them a better means of displaying the results when testing the migration.
4.4.1 1 Software definition and development A conceptual design for each software application was developed based on the reengineered work process. This generated the detailed requirements, leading to creation of a requirements document. The review process could then begin. After a requirements document has been created, it could be brought under configuration control so as to take account of any changes to requirements that could impact on the budget and/or schedule. Coding, testing, "bug fixing'?, and tracking could then take place. However, even after the application had been put into production, the development process would not necessarily be complete as new enhancements and changes in requirements could occur. Adherence to a development process is therefore critical.
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The software approach was to build an IM system based on an expandable framework (Figure 4.9). Applications such as LIST create lists that in turn create tables, graphs, and maps. This from the ERDB and spatial (ER Project GIs) databases, and is accessed only through the enterprise system. Instead of developing a monolithic application, the approach was to develop and deploy clearly defined software components that could be integrated easily into the overall system. These components fell into the following categories, the concept of the user applications being illustrated in Figure 4.9. These were identified as being user-critical during the requirement-generation and -review phases of the development cycle: 1) Data and Information Input Applications included: a. Sampling event planning applications b. Sample tracking applications c. Electronic Data Delivery and Data Checking applications d. Data Verification and Validation e. Data Quality Assessment
2) Data Reporting and Data Dissemination Application included: a. Data extraction and analysis applications b. Visualisation and mapping applications c. Decision management applications d. Web-based informal portal and reporting 3) Administration Applications included: a. Workflow Management b. Document Control System c. Database Administration Applications.
4.5 Conclusions and the Challenges Ahead Today's geoscientists are faced with the need to create a new paradigm that meets client and stakeholder requirements, by creating systems suitable for storing and retrieving data. They must close the gap between organisational goals that focus on a fact-gathering or R&D process and the customer's expectations, which focus on the need for more and better information. Tools are needed to retrieve this information easily, and ensure timely delivery. The LANL case study exemplifies successful implementation of the new IM paradigm. The ER Project successfully re-engineered its business processes while work was on-going. New work processes and electronic systems were built that better met their own needs and the needs of stakeholders. Readily accessible data, integrated data types, and improved data control and data quality not only satisfy user needs but also create audit tracking capabilities that can be used to defend management decisions. The IM paradigm thereby greatly reduces the risk of failure and increases stakeholder and client satisfaction. Such an infrastructure, including the documentation processes, can thereby be successfully created provided an organisation is willing to support these processes, providing a launching pad to accelerate complex environmental decision support.
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Figure 4.9. Concepts concerning user applications
The geoscience community at large is, however, still faced with many challenges towards shifting paradigms, including the following: Who defines our business processes? Who will freeze our system requirements (a management consultant, a geoscientist, or an IT professional)? Challenges in system architecture XML versus B2B for data gathering 2-tier versus n-tier architectures Thin- versus Thick-client applications Challenges in system design Relational- versus Object-oriented databases Business rule encapsulation and maintenance 3D application interfaces across the enterprise.
References Canepa JA (1999) Integrated Information Management Plan, Los Alarnos National Laboratory memorandum EIER:99.228 with attachment (ER ID 64099) D'Agnese F, O'Brien G (2003) Impact of geoinformatics on the emerging Geoscience Knowledge Integration Paradigm (Chapter 27, this volume) Darnton G , Darnton M (1997) Business Process Analysis, International Thomson Business Press ER Project Information Management Work Plan, August 1999 Information Delivery Incorporated (2002) Personal Communication with Messrs. Tim Mayes and Robert Dodge
5 Putting the user first: implications for subsurface characterisation A. Keith ~ u r n e r ' Delft University of Technology, Delft, The Netherlands also Colorado School of Mines, Golden, Colorado, USA
5.1 Introduction Successful use of new information technologies to improve subsurface investigations depends on accurately identifying user requirements, and also the associated social, economic, and policy issues. Many are familiar with large data collection efforts or information systems that failed, not because they were scientifically or technologically wanting, but because they just did not meet the needs of any significant user community. This paper is based on experience gained in applying 3D modelling for subsurface characterisation over more than 10 years.
5.2 Defining user requirements The problem may be expressed from the viewpoint of experience, which has shown that data alone are insufficient to most users. "Users want solutions, not data" and "Users want information they can use immediately in a form they can understand" are two fundamental truths. These truths explain why so many potential users of geoscientific information experience problems with what geoscientists produce. It would be reasonable to ask what indeed do geoscientists produce. Both data and information are produced, as traditional products and as thematic products. The traditional products include geological maps and a variety of highly technical scientific reports. These products are designed for use by specialists. Traditional reports frequently employ extensive technical jargon that provides scientific precision but cannot be understood by the non-specialist. Geological maps are based on a very specialised graphical method of representing 3D spatial relationships on a 2D piece of paper. They accomplish this while employing 19" Century-printing technologies - a truly remarkable feat. An increasingly wide variety of thematic products are being produced. These focus on a specific theme, or sometimes a market sector or particular type of user. Examples include reports on specific regions that emphasise the environmental conditions, historical aspects, or natural processes. Many are written in simple M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 61–68, 2003. c Springer-Verlag Berlin Heidelberg 2003
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language and include carefully designed and colourful graphics that appeal to many readers. Thematic products are produced in a variety of media, including digital products such as videos, CD-ROMs, and Internet access pages.
5.3 The problem with traditional products The problem with traditional products, specifically geological maps, is readily illustrated by a fairly recent example from the United Kingdom. In the early 1990's, the British housing insurance industry was facing catastrophic losses following a drought that caused clay soils to shrink and building foundations to crack. One underwriting group decided that geology might provide a guide to the scope of the problem and so purchased some standard geological maps from the British Geological Survey. Their reaction to these maps was that they represented "pretty colours and confusion" because they could not interpret them. Subsequent interactions with the British Geological Survey resulted in the development of specific products targeted to the user (industry) requirements, but the process was not without some difficulties and took quite a long time. Many, perhaps most, national geological surveys are faced with rapidly changing user requirements. When originally established a century or more ago, most geological surveys were expected to work with the mineral exploration industries to help provide the raw material resources required by the industrial revolution. Later, many of these surveys became responsible for assisting in the discovery of petroleum resources. Both of these activities required interactions and communications between geoscientists - some employed by the geological surveys, others serving on university faculties or employed by the exploration industries. Thus traditional products - geological maps and scientific reports served the communication functions well, and both data and information were disseminated.
5.4 Developing new products to satisfy modern users In contrast, the modern world has rapidly brought new demands and new users to the door of the geoscience data provider. The example of the British insurance industry described above clearly shows some of the resulting communication problems. Unlike the older resource industry based user community, the new users often cannot use basic geoscience data because they have no capability for interpreting it. In many cases they cannot evaluate the merits of alternative interpretations. When provided with a series of such alternatives, they become confused. They may also be unable to distinguish between theories and facts. In short these new users clearly reflect the two truths defined earlier - "solutions, not data" and "information in understandable form". Users can be classified in terms of their information acceptance capabilities as "Thick" or "Thin" clients (Figure 5.1). A "Thick client is one that can accept and interpret or evaluate a great deal of raw data. They desire a wide variety of data
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and information, and hence accept a "wide," or varied, and very rich datdinformation stream. Hence they can be termed "Thick" clients. In contrast, the relatively unsophisticated user only desires a relatively simple, concise answer to his or her question. A small amount of carefully selected data or information usually suffices to meet their needs. Hence, such users can be defined as "Thin" clients. Figure 5.1 also shows another important phenomenon. The thin clients are very numerous - they represent the broad public - while the thick clients are much less numerous.
Figure 5.1. The concept of thick and thin clients
Unless information systems are developed to serve the needs of these thin clients, support for the entire geoscience discipline by the public will cease to exist. If geoscience is largely ignored by society as being unimportant, funding will rapidly disappear, and thus so will the ability to serve the specialist thick clients. Without the thick clients, scientific advancement will stop. So the importance of making sure that "the user is put first" when designing the subsurface characterisation tools is of paramount importance.
5.5 Geo-Objects and subsurface modelling Kelk's description quoted in the introduction to Part A (this volume) refers to the concept of "Geo-Objects". What are "Geo-Objects" and what characteristics do they have that make subsurface characterisation and modelling so special? In short, why is it that geoscientists cannot just use CAD (Computer Aided Drafting) systems to model their subsurface features, just like engineers design and model complex man-made structures? Various geological features or units that exist in the subsurface can be considered to form Geo-Objects. Raper (1989) introduced the concept of a "geo-object" and noted that there are two distinct types - "sampling-limited geo-objects" and
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"definition-limited geo-objects". Features such as a rock layer or a fault plane, whose position in space can be better identified with additional samples, are termed "sampling-limited geo-objects". Ore shoots, sedimentary facies, or pollution plumes, which can only be visualised by first selecting a threshold parameter and level for inspection, are termed "definition-limited geo-objects". Both types of Geo-Object usually exhibit the following characteristics: complex geometry and topology scale dependency, including hierarchical relationships fuzzy, property-ruled boundaries may contain holes and separations may be embedded in other objects display heterogeneity and anisotropy of properties Thus geological modelling of the subsurface typically encounters geo-objects that have complex geometry and topology, and may have fuzzy definitions and scale dependencies as illustrated by the buried river channel sand shown in Figure 5.2. In contrast, most engineering design objects appear as in Figure 5.3. They are much more regular, homogenous, and well defined, and so can be designed with CAD tools.
Figure 5.2. A typical "Geo-Object"
Figure 5.3. A typical engineering object
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A critical 3D geological subsurface modelling problem is created by the lack of sufficient definitive data. Field observations, such as the boreholes shown in Figure 5.4, are usually very widely spaced. In order to build an acceptable model, the creator must interpolate between these widely spaced data points.
Figure 5.4. The fundamental model-building difficulty
5.6 Solutions to improve efficiency How can increased efficiency in model construction be achieved? Several potential solutions are provided in this volume. Many individuals and groups are testing new approaches concerning how best to build 3D models from isolated observational data supplemented by secondary inferred data, such as provided by geophysical surveys. Specifically, how can scientists connect the known conditions at the sampled locations to form a 3D model without the expensive and timeconsuming task of developing a series of digital 2D cross-sections? How can realworld complexity be modelled? How can the models be verified? How can the models represent uncertainties of the geological framework (i.e. the shapes, continuities and relationships of the geological features) and the physico-chemical properties within the units? Several research approaches are under way to resolve these questions. The key appears to lie less with developing new GIs tools and more with methods that "discover" the necessary parameters which control the observed geological framework architectures by using existing field observations and data sources. What appears to be required is some way to harness the experience and understanding of the geoscientist in the model-creation process.
5.7 Implementing new information technology Evolving information technology concepts are also providing tools critical to resolving the problems of subsurface characterisation. Historically GIs methods
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have evolved in isolation from corporate data bases. As shown in Figure 5.5, a "firewall" was erected between the spatial analysis tools provided by GIs and the corporate-wide financial and operational data. Only a very thin "pipe" passes through the firewall to connect the two data bases.
epartmental Data
Enterprise Data Database
Figure 5.5. Historical view of GIS Technology
There were, of course, good reasons for such a practice. As GIs developed, the existing database technology could not support the long transaction times involved in editing and updating spatial data files. The financial and business transactions undertaken with the corporate data base involved much shorter time steps. Faced with this dichotomy, the solution was to keep separate the GIs data base. This made the operations feasible, but at the price of placing the GIs data away from the centre, and also of introducing a host of problems concerning maintaining data integrity between the two databases. Fortunately, beginning in about 1995, the database and GIs technologies matured to the point that long transactions could be readily supported. Several vendors, including Oracle, began offering the ability to manage spatial data queries within traditional databases, while GIs vendors began to allow the use of multiple or dispersed databases by their products. There is thus now the opportunity to consider data management systems as illustrated by Figure 5.6 to support subsurface characterisation.
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Spatial Data Management
Fast Access to
Figure 5.6. Current technology allows integration of spatial and non-spatial data in an enterprise-wide database, or series of linked databases
5.8 Putting the User first As the modelling process evolves, and becomes increasingly supported and integrated within the new information technologies, the focus on the users must be kept in mind, not least to ensure their needs and requirements are recognised and satisfied. In order to satisfy the users of subsurface data, a "family" of distinctive delivery products must be provided, each targeted to a distinct class of users. It is necessary to distinguish between the needs and abilities of sophisticated users, including most "researchers", for example, and those of less sophisticated users. While some users can re-analyse or re-process original data, the users amongst the general public usually want an answer, not the original data, not least because they probably cannot process and may not understand it (the "pretty colours and confusion" regime). The problem focuses on how users can be helped to understand and apply the data, and be aware of its limitations. Geology maps, for instance, are highly interpretative documents. There is a real danger of producing wildly inaccurate products by combining diverse data sets in ways that exceed their scope. Beyond the technological issues, the question arises as to what are the major operational considerations. Experience suggests that the design of the user interface is critical. The expert wants to enter a request rapidly and precisely; and desires shortcuts. In contrast, the novice needs assistance to understand the options, perhaps with on-line help or tutorials. The novice user would be mystified by the "expert" interface; while the expert will become exasperated with the "novice" interface. Operational issues involve money. Who is to pay, and how much? The impact of the concept that the "user pays" is familiar, following its application to many services formerly provided by government and now privatised. Use of improved
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subsurface characterisation methods would similarly be greatly affected by financial and intellectual property climates.
5.9 Conclusions Experience to date has shown that good communication is critically important. Generic products are often insufficient and do not meet the needs of any user group particularly well. Fortunately the new information technologies provide mechanisms to place customised products in the hands of different users. However, the development of such new products requires collaboration.' The users must be involved in the creation of the products, and in the design of the user interfaces and delivery channels. While the data acquisition should remain in the domain of the geoscientific experts, the users must be given the opportunity to evaluate HOW and WHY data are acquired and processed. Fortunately the new technology provides the tools to perform subsurface characterisation FASTER, BETTER, and CHEAPER, just as society's demands for new and refurbished infrastructure increases the requirements for this information.
References Kelk B (1991) 3D modelling with geoscientific information systems: the problem. In: AK Turner (ed) Three Dimensional Modelling with Geoscientific Information Systems, NATO AS1 Publications, Series C-354, Kluwer Academic Publishers, Dordrecht, The Netherlands, 29-38 Raper JF (1989) The three-dimensional geoscientific mapping and modelling system: a conceptual design. Chapter 2 In: JF Raper (ed) Three Dimensional Applications of Geographic Information Systems, Taylor and Francis, London, 11-20
6 What are the end-user issues? Settlement risk management in underground construction Frank ~ a a l b e r ~Jacco ' , ~aasnoot',Holger ~ e t z e l ' ~ ~ 1
Witteveen+Bos Consulting Engineers, The Netherlands 2 ~ e l fUniversity t of Technology, Faculty of Architecture, The Netherlands
6.1 The North-South Line Metro Project Until now, metro construction in Amsterdam used cut and cover methods that required the demolition of many historic structures. This resulted in great public opposition to any further expansion of the system. However, increasing population and traffic congestion within central Amsterdam have led to the city council undertaking studies to see if new methods could create a metro without significant surface disruption and to offer complete protection to historic structures. These studies have revealed that it is feasible to construct a metro in the soft-soil conditions which underlie Amsterdam, by using innovative mechanized shield tunnelling methods. However, these studies also emphasized that project success requires careful and extensive subsurface investigation~, intensive numerical modelling to predict risk, innovative Tunnel Boring Machine (TBM) design and operations, and real-time monitoring of critical conditions during construction. After careful consideration of these studies and weighing the alternatives, the city council of Amsterdam decided in November 1996 to permit construction of a new underground metro line, the North-South Metroline. The line is to be constructed in two phases and, when completed, will extend from the north of the greater Amsterdam region to Schiphol International Airport, south of the city. The first phase of the project will be about 9 kilometres long (Figure 6.1). The section north of the IJ river will run along the surface. An immersed tube construction will be used to pass under the IJ river and the Amsterdam Central Station complex. The line then traverses the historic heart of Amsterdam to reach its southern terminus at the SouthAVorld Trade Centre (SouthIWTC). When this phase is completed, in 2008, it is anticipated that it will be capable of transporting around 160,000 passengers per day. Constructing the section between Central Station and SouthIWTC under the historic city centre will be particularly challenging. At least 3.15 kilometres of the total section length of 3.8 kilometres must be constructed using advanced TBM technology in order to avoid surface disturbance and damage to historic buildings. Two individual single-track tubes with an inner diameter of 5.82 m and a bore diameter of ca. 7 m must be constructed at depths between 20 m and 31 m. M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 69–83, 2003. c Springer-Verlag Berlin Heidelberg 2003
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Figure 6.1. The Amsterdam North-South Metroline
Legal stipulations require the tunnels to mostly follow the present street pattern and thus avoid passing under houses or other buildings. However, the narrowness of the existing streets requires the tunnels to be closely spaced, separated by only about 0.5 tunnel diameters, and along one section they must be stacked vertically. In spite of such measures, the minimum curvature allowed for the trains (190 m radius) means that the tunnels will have to pass under a few buildings. Constructing the section between Central Station and SouthIWTC under the historic city centre will be particularly challenging. At least 3.15 kilometres of the total section length of 3.8 kilometres must be constructed using advanced TBM technology in order to avoid surface disturbance and damage to historic buildings. Two individual single-track tubes with an inner diameter of 5.82 m and a bore diameter of approximately 7 m must be constructed at depths between 20 m and 31 m. Legal stipulations require the tunnels to mostly follow the present street pattern and thus avoid passing under houses or other buildings. However, the narrowness of the existing streets requires the tunnels to be closely spaced, separated by only about 0.5 tunnel diameters, and along one section they must be stacked vertically.
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In spite of such measures, the minimum curvature allowed for the trains (190 m radius) means that the tunnels will have to pass under a few buildings. This section also includes three deep stations. In order to minimize surface disturbance, these stations will use covered excavations. Diaphragm walls will be placed to depths as much as 40 m, followed by placement of a roof slab. Then the street environment can be returned to its previous condition while the excavations are continued within the walls and under the roof. This combination of TBM tunnels and station construction methods satisfy the goal of minimizing disruption of normal surface activities. However, prevention of surface deformations and damage to buildings and existing utilities requires careful facilities design and management of construction operations based on detailed knowledge of subsurface conditions. The Amsterdam North-South Metroline project thus provides an excellent example of how the success of modern urban underground construction depends on accurate knowledge of shallow subsurface conditions.
6.2 Geological Characteristics of the Subsurface The subsurface of Amsterdam is composed of a thick sequence of Holocene and Pleistocene sand, silt, clay and peat layers (Figure 6.2). The oldest Pleistocene deposits are marine clays and fine-grained sands that currently extend far below the surface - some 250 to 350 metres deep. Tongues of Saalian glacial ice eroded a deep basin into these deposits. This basin was subsequently flooded by the sea and partially filled with marine sands and clays (the Eem Clay - Figure 6.2). During the last glacial period, the Amsterdam region was subjected to a periglacial climate and the basin was filled with sand (the Second Sand Layer - Figure 6.2). Younger Holocene deposits consist mainly of peat and clay formed under the influence of an initially rising sea level. About 5000 years ago, a lowering of the sea level allowed rivers to create channels in the existing deposits. These channels, sometimes with widths of over 100 metres contain thin sand and soft clay layers, caused by infilling during to the continued rising sea levels of recent times. The result of this geological evolution is a fairly consistent stratigraphy of sand, clay and peat layers, as shown in Figure 6.2, with occasional incised river channels. Historical buildings are placed on wooden piles about 15 metres long that are founded in the First Sand Layer. Larger, modern buildings are founded on concrete piles driven to the Second Sand Layer. Tunnel driving will take place almost completely in the Allerod, Second Sand, and Eem Clay units. The tunnels will not directly impact the older shallow pile foundations and, since the locations of the modern deeper piles are well known and not very frequent, avoidance or remedial measures can be taken during construction as required. The Eem Clay has special significance for the overall advancement of the tunnel. It also will be encountered in the excavations for stations, where it will interact with the braced diaphragm walls. The characteristics of the Eem Clay were relatively unknown before the NorthISouth Line project. Consequently, considerable geotechnical investigations were undertaken.
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An important geological consideration is the presence of groundwater beginning about 1 metre below the ground surface. The tunnels and station diaphragm walls are thus subjected to water pressures of around 3.5 bars. Disturbance of the groundwater regime cannot be allowed. Dewatering would likely immediately cause unacceptable ground deformation, but also it would cause deterioration of the older wooden piles. These piles are protected as long as they are totally immersed, partial exposure by dewatering will cause them to decay.
Em Clay
Glacial Clay Layer
Figure 6.2. Geological stratigraphy typical of Amsterdam.
6.3 Creating a Three Dimensional Ground Model Dutch engineering practice typically creates a 2D cross-section interpretation by projecting field borings and CPTs (Cone Penetration Tests, Dutch cone or soundings), onto a pre-defined vertical cross-section. When dealing with linear facilities such as roads or tunnels, this cross-section is usually aligned along the trajectory of the project. The observed conditions at each boring location are then classified into standard geological or geotechnical units and these are correlated throughout the cross-section to provide a graphical view of the positions and continuities of
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all units. Planning for the North-South Line project required a more complete view of the three dimensional variations of the subsurface, not only along the centreline of the tunnels but also extending some distance to each side. A 3D Ground Model application, called 3DGM, was developed for the NorthSouth Line project by Witteveen and Bos Consulting Engineers, the project managing consultant. 3DGM was developed as an extension to the Archnfo GIs package, and can automatically create a three dimensional model of the geological strata forming the Amsterdam subsurface. The approach utilizes a standard definition of the vertical sequence of geological strata (indicated as "Main Layers", or "Hoofdlagen" in Dutch) that can be expected in Amsterdam. For instance, in Amsterdam the Base Peat layer is always on top of the First Sand layer. Such sequences are determined from basic geological knowledge, and are applied as boundary conditions by 3DGM. Because, as noted earlier, the basic strata under Amsterdam are sometimes cut by old river channels, it often happens that very local layers are encountered, for instance a clay lens within a thick sand layer. 3DGM allows the user to identify and manually adjust the geometries of such local lenses. The fact that the model is mostly generated automatically makes the input and processing of new information relatively easy. This 3DGM model defines conditions within a 3D subsurface volume. Geotechnical cross-sections can then be defined along any desired orientation and at any desired position. Such geotechnical cross-sections are generated automatically. Cone resistance values (CPT or SPT, depending on the type of site investigation) and codes defining the geological/geotechnical layers can be plotted on the geotechnical cross-section (Figure 6.3). This makes checking for errors and maintaining an up-to-date geotechnical model relatively easy.
Figure 6.3. Example of a geotechnical cross-section processed within 3DGM, including some CPT logs
Visualisation of subsurface data is one of the most important applications of 3DGM. Cross-sections provide one method of visualisation, as illustrated in Figure 6.3. Contour maps are another 2D display method. 3DGM can create contour maps of the thickness of individual model layers, or of the top or bottom eleva-
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tions of particular layers. 3DGM can also produce 3D displays (Figure 6.4). These displays are especially illustrative when the layering is presented in relation to the civil engineering construction. This type of data presentation can lead to an understanding of the ground-construction interaction that was up to now not possible.
Figure 6.4.3D visualisations of subsurface conditions encountered in station excavation
The 3DGM model of the subsurface is especially suitable for volume calculations. The volumes to be excavated, subdivided by each geologic unit and construction stage, are readily determined. Figure 6.5 illustrates the calculated volumes of various material types that would be excavated at each construction stage for one of the stations.
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10000
Excavated Soil (m31
I
Figure 6.5. Example of volume calculations using 3DGM
Profiles created by 3DGM can be exported to geotechnical calculation software packages for further modelling and prediction. For example, the Mserie analytical software developed by GeoDelft can analyse 2D layer representations along specified profiles to calculate settlement and design directional drilling programs. The Plaxis Finite Element Modelling software accepts a 2D profile to define layers in an element mesh. Generally these numerical applications require much smaller numbers of points than exist within the 3D subsurface model, so the number and density of points can be defined during export from 3DGM, with more points in the zones of interest and less outside these zones.
6.4 Numerical Modelling of Tunnelling Effects Approximately 1500 historically important masonry buildings, supported by more than 20,000 pile foundations extending to the First Sand layer, are located along the central section of the proposed North-South Metroline. In addition, the proposed tunnel passes below a number of canal bridges and quay walls. A tram line runs along the streets over the entire tunnel boring section and several underground structures are located directly over the tunnel axis. All these are sensitive to surface deformations, so the North-South Metroline design team had to investigate the risk of settlement-induced damage. Unaccept-
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able damage was defined as including not only any kind of functional or structural damage, but also aesthetic and repairable damage. The adopted criteria specified that crack widths could not exceed 2 mm. A typical masonry structure within the expected influence zone of a TBM tunnel consists of a block of several masonry houses. The house units are connected to one another by common masonry load bearing walls and continuous (front and back) facade walls. The connections between the load bearing walls and the facade walls consist of anchors or teeth constructions between the masonry bricks, which can transmit vertical shear forces between the construction elements. Thus the block behaves structurally as a single construction. An individual house unit is about 7 m wide, about 16 m high and about 18 m long. The load-bearing masonry walls are founded on paired pile-groups placed under wooden cross beams. The pile groups are spaced about 1.2 m apart and the two piles in a group are about 0.5 m apart. These wooden piles are driven into the first sand layer, and are primarily end-bearing - about 80% of the total load (of about 80-100 kN) is transmitted through the pile tip.
windows
'-.
Temporary longitudinal trough along the f a m w a lI...-'
Figure 6.6.3D model of the block of ten masonary buildings (Netzel, Kaalberg, 2000)
A standardised block of ten masonry houses on pile foundations was used to model building 3D deformations and reactions to ground movements caused by passage of the TBM (Figure 6.6). Netzel and Kaalberg (1999) describe how the GIs capabilities assisted such 3D numerical settlement risk assessment studies.
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The results of the study were used to define the boundary conditions and the operating requirements for the tunnelling activities. Program DIANA was used to evaluate the behaviour of masonry walls, the interaction between soil and structure, and the 3D geometric disturbances to the buildings (Netzel and Kaalberg, 2000). The masonry walls were modelled as non-linear plane stress elements, front and rear facade walls included door and window openings. The pile foundations were modelled as non-linear springs to reflect the interaction between soil and pile, including both pile toe resistance and skin friction. The first shield-drive project in Holland, the Heinenoord tunnel, was used as a large-scale testing ground for verification of the theoretical model calculations. The simulations began with the loads existing prior to the TBM passing the building. Then settlements at the pile toe level imposed by the passing TBM were modelled for a large number of load steps so as to simulate the advance of the 3Dsettlement wave along the building. Figure 6.7 shows four stages of 3D deformation behaviour of the standard building block. Passage of the settlement wave causes the building to twist because parts of the building are already undergoing settlements while other parts are still in the initial situation. Even after the passage of the TBM, the imposed settlement trough remains and the building may be deformed toward the tunnel axis.
Figure 6.7. 3D deformation stages during and after passage of the tunnel (Netzel and Kaalberg, 2000)
An extensive parametric study was carried out and used to define specific damage criteria for masonry structures of Amsterdam (Figure 6.8). These studies evaluated:
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Material parameters of masonry walls (including E-modulus, tensile strength, brittle and plastic crack behaviour) Settlement trough geometries caused by different volume losses and different depths of the tunnels Location of the building within the settlement trough - its distance to the tunnel Geometry of the end bearing walls. The load bearing walls at the ends of many blocks may have openings for doors and windows. These transverse end walls are referred to further as side facade walls. Thus far only the passage of a single tunnel has been considered. Since twin tunnels will be constructed, the modelling included settlements caused by passage of the second tunnel. The results of these studies clearly showed that the damaging effects of the TBM are considerably reduced when the depth and distance to the tunnel is increased, or when the volume loss around the TBM excavation is decreased (Netzel and Kaalberg, 2000). This last aspect requires greatly improved TBM performance, and led to the third stage of research for planning the NorthSouth Line. Variation of the of Ihe end
Massive masonry end bearing wall
MaSm wan as end bearing wall {side f a w e wall)
--.._
Varlatlon d 3D-mttbment for d~fferentvolume losses and UrHerent funnel dept&-
Figure 6.8. Parametric variations (Netzel and Kaalberg, 2000)
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6.5 The Integrated Boring Control System (IBCS) Initial feasibility studies for the North-South Metroline revealed that existing state-of-the-art TBM methods in soft soils produced a 1-3% volume loss around the tunnel. This would produce excessive surface deformations and building damage. Because extensive mitigation along almost the entire length of the route would be too expensive, the only alternative was to focus on improving the quality of the TBM process. Analysis of potential innovations to TBM techniques identified a compact version of the variable (vario) shield system that could be adapted to meet the project requirements (Figure 6.9). This shield could assist in reducing the degree of volume losses and subsequent surface deformations (Kaalberg and Hentschel, 1998; 1999). However, the analyses also showed that the use of such a high-performance shield system would provide only a partial solution. The minimal surface deformations demanded by Amsterdam could only be achieved by precise operation of the TBM system, and this depended upon effective process control. This understanding logically led to the consideration of a process control system concept that has been named the Integrated Boring Control System (IBCS).
Figure 6.9. Vario shield system
The IBCS design uses information concerning ground conditions as well as monitoring the operation of and the effects caused by the TBM. In this way interactions with the soil and the pile foundations can be constantly evaluated and potential damage to buildings controlled. Diverse open- and closed-loop control systems are already used by current tunnel boring systems. However, the IBCS concept is distinctive in that it integrates settlement information into the control parameters.
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This allows the tunnel boring process to be controlled not solely on the basis of tunnel and machine data. Rather, lBCS control and operational criteria reflect settlement information provided in real-time as the TBM advances. Figure 6.10 illustrates the basic IBCS process and its relational links to data acquisition and the tunnelling system operations. The initial version of IBCS contains three major components - an Effect Measurement component, a GIs Database component, and a Prediction component. These are shown in the lower right portion of Figure 6.10. The Effect Measurement component accepts and integrates real-time data from the TBM (TBM and boring process parameters) and from the surroundings (monitoring systems in the ground and on piles and buildings). These data sources are shown across the top of Figure 6.10. The crucial point is that real effects are provided to the IBCS. Modern measuring processes, linked to the IBCS on-line, provide continuous and simultaneous data acquisition for all monitoring sites. Relevant boring process variables are extracted from TBM measurements. Together, these data streams allow the IBCS to evaluate the real process as the tunnelling progresses. The GIs Component performs three distinct functions. First, it contains and maintains the 3D subsurface geological and geotechnical models described in the previous section. Second, the GIs component accepts information obtained and processed by the Effect Measurement component and uses this new information to up-date and improve the pre-existing 3D model (Netzel and Kaalberg, 1999). Third, the GIs provides accurate visualizations required for rapid analysis and interpretation in the Prediction component. RML PROCESS
I SETTING OF
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VIRTUAL EFFECT -REAL EFFECT ENVIRONMENT
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Figure 6.10. Process structure with the Integrated Boring Control System (IBCS)
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The Prediction component performs predictive calculations and comparisons. Prior to the actual tunnel construction, a virtual boring process is modelled using a 3D FEM numerical model that incorporates determinative process parameters for the TBM and knowledge of the subsurface conditions surrounding the advancing tunnel. This numerical model evaluates the reactive forces in the bore front and annular void areas and assesses how these will be transmitted into the surroundings. This permits predictions to be made concerning soil deformation and settlement. The Prediction component also uses the 3D subsurface model maintained within the GIs component to combine predictions based on numerical models with current observations. Simultaneously the boring process settings are being monitored and displayed. The result is a closed, real-time information circuit for the IBCS. In the initial development stage of IBCS, the Decision-Setting functional unit is a purely information and visualization system that monitors the TBM processes and extracts relevant process variables. Adjusting the process parameters while observing the actual effect data permits the process sequence to be successively optimised so as to minimize settlements. Decision-making and adjustment must continue to be carried out by humans, acting as an expert team. This is illustrated on the lower left portion of Figure 6.10. Real-time visual displays of the boring process variables, along with data defining the predicted and actual settlements, will be produced at the TBM control panel. These displays provide the basis for decision-making by the process control team. It is anticipated that subsequent versions of IBCS will incorporate calculation algorithms to produce a more nearly fully automated process control system. However, even with this semi-automated operating mode, the IBCS demonstrates the importance of creating and using 3D subsurface models. The displays utilize previously conducted numerical models, as well as current observations. Interpretations of both models and observations depend on the 3D subsurface model. When combined with experience in TBM operations, and knowledge of the importance of various process variables and operating parameters, this initial IBCS can result in greatly reduced settlements caused by TBM operations. It allows TBM operators to make better and more rapid operational decisions, based on an extended experience base, while also reflecting both site-specific and real-time conditions.
End-User issues The development of TBMs has evolved from how these machines can operate efficiently in diverse ground types (from rock to soft soils) to how TBM operations can be optimised to provide minimal ground deformation, especially in soft soils. In a vulnerable urban environment, such as along the central section of the Amsterdam North-South Metroline, the question becomes what TBM performance is required and what level of performance can be achieved. Then questions arise concerning the quality and quantity of the subsurface geo-information required to achieve this high performance level.
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The North-South Metroline project undertook an extensive site investigation programme in order to establish the relevant geotechnical units and their parameters. The project determined that a high-quality 3D subsurface model was required to represent the layers of geological and geotechnical units beneath Amsterdam. The 3DGM software was developed to fulfil this need. Previous tunnel projects used only one large-scale longitudinal geotechnical profile to characterize the subsurface conditions. The North-South Metroline project used 3DGM to create a true 3D representation of the subsurface along and adjacent to the entire project. Because 3DGM was linked to a GIs a powerful and easy-to-use 3D model was available to provide high quality information during all project phases. Processing and updating of the model involves automated processes. Instead of using the traditional single centre-line section, 3DGM allowed the project engineers to generate custom cross-sections at any location and orientation desired. This flexibility gave the engineers an opportunity to make more sophisticated analyses. For example, cross-sections were developed with 10 m spacing along the entire project length. These were used to provide subsurface conditions to numerical models predicting settlements, and building deformations. However, one should always be aware of the 3D subsurface model reflects the quality of the input data. In urban areas, the choice of locations for ground investigation is generally limited by the existing land usage, so the 3D model produced by 3DGM, or any other method, may have limited accuracy in at least some locations.
TBM process control
Information on the geo-environment
Figure 6.11. Relationship between degree of TBM control and amount and quality of geological information (Haasnoot and Maurenbrecher, 1997)
Figure 6.11 schematically gives the relationship between the degree of TBM process control and the amount and quality of information on the geological environment (Haasnoot and Maurenbrecher, 1997). In the design stage, only a limited amount of geological information, for instance basic soil or rock conditions, results in a rapid increase in the degree TBM process control, and the curve rises rapidly. The addition of more geological information, often involving much more detail and consequently much more effort and expense to obtain, in the later con-
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struction stage may only provide more modest improvements in the degree of TBM process control. Thus, the challenge for the geological professionals is to determine whether ground investigation can be enhanced so that a significant increase in TBM process control can be achieved. From the users point of view, such considerations suggest that the best strategy for the acquisition of geological information should be guided by an approach that can be called Value Ground Investigation. This approach suggests that additional efforts should be expended to acquire information only if that information can be expected to immediately enhance the control of the construction process. This approach can be established by integrating an appropriate site investigation tool or indicator into the construction process. If used correctly, the approach will provide high quality information at the exact location of interest. To use this newly acquired information in the construction process, models or algorithms will have to be developed that are capable of processing this information in real time and advising the construction process of appropriate modifications or adjustments. Based on experience with the Amsterdam North-South Metroline, it appears that this Value Ground Investigation should form the paradigm for the geoscientists for the coming decade.
References Kaalberg FJ, Hentschel V (1998) Tunnelvortrieb in weichen Boden mit hohem Wasserstand - zur Entwicklung einer setzungsorientierten und setzungsminimierenden Betriebsweise der TBM. [Tunnel boring in soft soil with a high water level - development of a settlement-oriented and settlement-minimizing operating mode for the TBM.] Forschung und Praxis 37, Neue Akzente im unterirdischen Bauen. Diisseldod Germany Kaalberg FJ, Hentschel V (1999) Tunnelling in Soft Soil with a High Water Level and Piled Foundations: Towards the Development of a Settlement-Orientated and Settlement-Minimizing TBM Control. Proc. World Tunnel Congress'99, Oslo, Noway Haasnoot JK, Maurenbrecher PM (1997) Relevance of geo-information in designing bored tunnels in soft soils of the Netherlands. Proc. 14th lnternational Conference on Soil Mechanics and Foundation Engineering (6-12 September 1997), Hamburg, Germany Netzel H, Kaalberg FJ (1999) Settlement Risk Management with GIs for the Amsterdam NorthlSouth Metroline. Proc. World Tunnel Congress '99, Oslo, Norway Netzel H, Kaalberg FJ (2000) Numerical Damage Risk Assessment Studies on Masonary Structures due to TBM-Tunnelling in Amsterdam. Proc. GeoEng2000, Nov 19-24, Melbourne, Australia, vol2, 183 (CD-ROM) Van Dijk B, Kaalberg FJ (1998) Geotechnical Model for the North/South line in Amsterdam. Proc. 4th European Conference on Numerical Methods in Geotechnical Engineering, Udine
7 Data requirements for geo-environmental management: end-user and producer perspectives for a harbour Rodney L. Stevens Goteborg University, Sweden
7.1 Introduction Of particular relevance to harbour management are the societal demands for environmental care, both within the harbour itself and in its vicinity (ABP Research, 1999). However, the economics of harbour operation do not encourage individual contractors or government agencies to fully consider the long-range effects. An additional problem for environmental monitoring of harbours is the lack of consistent documentation of basic sediment-related features. Integrating the interests of science, industry and social sectors is clearly desirable from a holistic viewpoint, but is limited by lack of compatibility of data and documentation. The quantity of data now being produced by modern digital instruments increases the extent of this problem. There is now a need to define an appropriate database structure (Burton et al., 1999) and to establish recording routines which can permit flexible use within future, yet-to-be-defined contexts.
7.2 End-user and provider perspectives Sustainable management regarding silting and pollution in harbours is essentially focussed upon the following questions: 1. "Where, when, and how much?' 2. "With what effect?" 3. "How to evaluate the alternative preventive or remedial actions?"
Such questions can be addressed by extensive monitoring, essentially attempting to measure the parameters influencing each question for each site and occasion. The other extreme is to try to theoretically predict the effects of the physical and geochemical processes involved. Either approach is limited by lack of feasibility as well as economic restrictions. A hypothesis is now presented that an optimum balance can be achieved between experimental control and site relevance, combined with the predictive value M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 85–95, 2003. c Springer-Verlag Berlin Heidelberg 2003
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of process studies together with the necessary confirmatory documentation achievable by monitoring, i.e. site-specific measurements (Figure 7.1).
EXPERIMENTAL CONTROL
//
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SITE RELEVANCE
Figure 7.1. The combination of processes studies, modelling, and routine monitoring must be balanced by cost considerations, where none of these individual activities are economically optimal nor environmentally responsible (after Newson, 1992)
In addition to offering cost effectiveness, the application of modelling to combining the processes of evaluation and monitoring will help safeguard the environment since a single investigation cannot alone measure the response of the system to future changes or indicate when threshold conditions might be exceeded. Furthermore, a site-specific, engineered approach to harbour management would tend to address the individual problems associated with each project. The danger of such an approach is apparent when processes are interrelated and the system response less predictable than suggested by simply expressed relationships. "Experimental control" refers broadly to scientific evaluation. In the case of a large-scale natural system this may involve comparing the influences and effects of processes recorded in similar environments or changes recorded over time within one setting. In this respect, sediment investigations have a major advantage over many other types of environmental monitoring in that sediment accumulation represents a time-integrated, net effect of environmental processes. Sediment sources, transport pathways and accumulation rates can be estimated using water-current measurements, data from sediment traps or tracer studies. However, such methods are time-limited and generally become less accurate as the intensity of the processes involved increases, such as during storms or intensive ship traffic, perhaps the most decisive time for their total influence upon the environment. A natural filtering of the temporary changes and a record of the summed effects over yearly or longer cycles should complement the short-term measurements. The value of sedimentological studies would contribute to the strong relationship that normally exists between particle size, organic-matter content and geochemical reactivity, including the level of heavy metals and organic contaminants (Hikans-
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son and Jansson, 1983; Salomons et al., 1988). Since natural processes operate at different scales and magnitudes, in both time and space, and interact with man's utilisation of his environment, it is imperative to recognise those factors that can be manipulated and those that cannot. For instance, the prediction of siltation and the evaluation of pollution risks are essential tasks contributing to harbour sustainability if the sediment sources, transport pathways and the sites of accumulation can be identified and to some extent quantified.
MODELLING
c PROCESS
MONITORING
Figure 7.2. The priorities within a single harbour investigation are often related to relatively short-term goals, as illustrated by the 8 examples (circles, explained in the table below). The arrows (A-C) indicate motivating considerations for a more balanced use of activities investigative.
E n d - u s e r Objectives (illustrating typical input combinations)
I Motivating Criteria for Multi-Functional Assessment A Need for greater predictive capacity, both spatially and over time. B Need for greater data control and basis in theory. C Need for greater applicability with changed conditions.
I
I
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The approach to evaluating the routine monitoring can also be considered to represent the perspectives of an end-user, whereas process studies correspond essentially to the scientific producer perspective. The premise pursued here is to combine these perspectives, leading to a more complete basis for evaluation and improved cost effectiveness for harbour geo-environmental management. Such conflicts of interest are typified by the history of dredging in the New York harbour (Steward, 2000). The methods of sedimentological study need to be adapted to the harbour setting. These methods should be standardised as much as possible for each stage of the study, from planning the sampling procedures through laboratory testing to interpretation and dissemination. The choice of routine procedures needs to be based on detailed surveys at selected sites, aiming to estimate variability in the sediment parameters. This should lead to a simplified sampling scheme; indeed, the use of proxies for sediment characteristics may be justified in subsequent investigations. The flexibility to incorporate new aspects is nevertheless necessary within any documentation procedure adopted.
7.3 Harbour site investigations The inclusion of scientific goals within a monitoring programme is possible with prudent choices regarding sampling and documentation routines and by selective complementary analyses. The additional costs of each new task are necessarily an issue, and the overall environmental and economic advantages are the motivating arguments that need to be more clearly specified for harbour management. Only a few of the suggested methods listed in Table 1 are now taken up, dealing with sediment documentation for immediate and archive purposes, priority sediment parameters, and evaluation techniques. Others methods are not necessarily unfavourable, but any site investigation programme will have a similar cost-benefit evaluation for method selection. 7.3.1 Documentation Methods The balance between achieving short-term goals and the future utility of sedimentology data is governed by the choice of sampling and documentation protocol. The spatial coverage will necessarily limit the overall applicability of the database. Rapid (inexpensive) methods are preferred, particularly if information can be recorded without the need for interpretative input. Digital photography of box-cores meets these criteria (Figure 7.3). Box-coring is suitable for fine-grained sediments in relatively shallow water, where surface disturbance can be detected and visually controlled and, if necessary, complemented by parallel, piston cores. If intact, the sediment surface area (ca. 0.2 m2) observed in box-cores is significantly greater than obtained by other coring techniques.
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Functional facies (representing environmental conditions on the sea floor) can be classified using sedimentological and biological features, but such a classification can be applied later if the upper surface and the vertically sectioned box-core sediments are photographed. The relatively large quantity of sediment also facilitates sub-sampling for laboratory analyses and geotechnical testing from specific intervals. The turn-around time for sectioning, photographing and sub-sampling and clean-up is approximately 10-30 minutes once the box-core is recovered, often comparable to the time required for travel to and positioning at the next site. Tabla I. Selected technioues thaf warrant greater utilisatzon in harbour sediment -
i n ~ ~ d i g a L i onn ~e .motivating considerations and limitations are consideredfrom dzflerent pnrspuctivss.
End-user I Scientific Perspeclive Perspeclive Inenxnsive. Allows I Samuhzcan distutb sed&en& Records valuable Box-coring and extehm samphgof s o n n t m n for futun. digital imagery swecifii hk bib incorporatedmto routines. unkmwnpqcses. I Sediment compositm and Reauucs vew httle space Smear slides and andtraining..~ime-related p i n size can iater be sediment changes are recoded as estimated andevaluatedby archives baseline infomtion specialists. Priority Sediment I wameters Oenedvrecoenised as l An essentialpanuneterfor Documentation Methods
I
1I
Particle size
Organic matter content Component-specific geochemisty Evaluation Techn Grain-eke interpretations of sediment transpor; MuE-parameter normabation Functional Facies Classification (sediment types)
I
Multi-Functional Perspeclive I Documentation of multc dmlpLnaryfeatares feasible for n o n s a h t s %e .*o I Bottom s'urfwe Class. l Futun t e c h w s may hkely iprrea~ethe &lue of ;mall sample archives. Compkm e n t q to lab analyses.
I
I To h
I
I
1
1
be homogeneous withinan compatisons.Automated ewironment. Some analvses d v s e s and entha dist&uI tm&co~& ate pnfemd sho&i&ays bt ml&d S u n h arzuments as am I Malor dluence upon ndox size. above. Loss onl&tion I conbitmns. s t m i is only awmximate, but may cornlation withc~ntamnf0llowin~s~te. mts, but .hw i h p l f r k k specific calibration. I sizevadations. 1 Metal speciationand organic Relatively expensive and specitluation is necessary for mquins expertise for evaluation. Metal eciation evaluating the bio-gcousually not includa?Iin chemical process fleeting mutine sump. contaminants. rues %q&s e$ertise, but Goodconnectionbetween prwkles ormatlonofan process inte~pretationsd important net effect. monitoingdatabwes. Panmeter relationshipsmay I Site-specific associations Iaccouked o b p allow use of proxies fbr effective rouhne pmcednres. I sedimentologicdh a . I A b w s sciantfi mluation Com~rehensivearchive func6m without strict site s m y technicdreauinments.
I
e costs, smear-slide
ratas) -be used once xelawhms are established. ID@al a r c h e s posslble I OM s an essentd lur 1betweenbhlogid, sedi mentobgical l;ad wchemd evaluations See also I geochemistry.bebw. I T k spemti1cationof chemical components is important for understandingcontaminant mobility and other sedimentq conditions.
Pmvides abasic descriptions net ttranspolt conditions.
I Diverse practicalapplications I Combines whwical and m-
I chemical a i n some c k Ibiological observations.
,
I Stresses the interntion of
I
p h p i c a gcochekcal and biobealfactom
The sediment surface reflects the recent environmental conditions, especially regarding transport and depositional processes, but also nutrient supply and other influences upon the bottom fauna. For example, the presence of a filter-feeding fauna implies a certain supply of detrital organic particles, but with limited reworking at least moderate sediment firmness, and aerobic conditions. With further study in a particular environment, the Functional Facies classes (see also below) could be associated with more specific features regarding water salinity, current activity, pollution levels (cf. Salazar-Jimknez et al., 1982; Boyd and Honig, 1992) or redox conditions of the sediment (e.g. oxic, mixed, or anoxic; Myrow and
90
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Landing, 1992; Rosenberg et al., 2000). In turn, surface colonisation has a well recognised importance for current baffling, enhanced sedimentation, and stabilisation (e.g. ~ l m a set i al., 1987).
Dating Biostraiigrslphy Sediment description > facies Bulk element concentation Bulk density Total organic carbon, N,H, S Grain size
Mineralogy
Plasticity, shear strenght and viscosity PCB Sequential extraction
on board
in the laboratory
Figure 7.3. An illustration of complementary sampling, sedimentological documentation and sub-sarnpling for various subsequent analyses (Stevens et al., 1999)
A second type of sub-sampling is suggested for microscopic work and for longterm archives. Smear slides are traditionally used for deep-sea research (e.g. Rothwell, 1989). A very small amount of fine-grained sediment is "smeared" and affixed to a glass microscope slide with epoxy or similar mounting medium. The slides are appropriate for estimating particle size distribution, particle texture and composition (mineralogy, organic matter character, biological constituents) and, importantly, the aggregate and other structural associations existing within the different components. Smear slides can be easily stored for later utilisation if finance, expertise or instrumentation is not immediately available. Environmental change can only be appreciated through temporal comparisons. In this respect, future questions may arise that are not immediately important or apparent. Future theoretical or technical developments may permit new investigations using the small amount of material stored in a smear slide or sediment archive (Stevens et al., 1999). It is recognised that even the most ambitious documentation may still prove unsatisfactory. Sediment archives should therefore be given a high priority within the project management programme. 7.3.2 Sediment parameter priorities Although there are numerous parameters that are valuable for sediment characterisation, most of these are quickly excluded from site investigations because of cost or time limitations. On the other hand, restricted measurements of chemical or geotechnical properties concerned with pollution or construction projects may ad-
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versely increase the risk of erroneous sediment treatment. In addition to the visual documentation of box-core sediments suggested earlier, a few detailed analyses of selected sites would strongly enhance the documentary value of any investigation. The sediment parameters having greatest influence on measuring the degree of pollution are (Table 1): particle size, organic content, and component-specific geochemistry. Particle size is essential for assessing sedimentological processes involved with transport, accumulation and post-depositional change. Since much of the organic matter is transported together with the finest, mineral particles, the clay content derived from a particle size analysis is often closely correlated with organic content. Both clay and organic matter are important because their large surface areas provide effective carriers for adsorbed pollutants, and their distribution in the environment is related to the prediction of contamination (Buckley and Cranston, 1991). The interpretation of transport pathways is facilitated by considering the successive changes in particle size between adjacent sites (McLaren, 1981; Gao and Collins, 1991; Stevens et al., 1996). This methodology is particularly useful in environmental studies because it reflects the time-integrated, net effects of many complicated processes that are difficult to measure and model representatively. Total-sediment geochemical analyses do not necessarily give an accurate measure of pollution risk since the bonding character of each contaminant influences mobility and reactivity. The natural background occurrences of metals can vary, masking the contributions from anthropogenic sources. Thus a certain level of geochemical documentation of specific components is desirable to evaluate their individual contribution to the total-sediment geochemical composition. The geochemical "speciation" of different components is done by sequential leaching of the sediment using increasingly strong fluids (Tessier et al., 1979; 1982). The operationally defined components are the exchangeable phase, carbonate phase, oxides, sulphides, organic matter, and residual phases. Size-specific fractions (e.g. clay or 0 is accepted when handling time-dependent process equations. A cell is one of the simplest possible topological objects; it is any figure that is topologically equivalent to a ball. The topological properties of a ball are: (1) it occurs as one piece (i.e. any attempt to divide the ball into more than one piece would require cutting, and that is forbidden in a continuous transformation), and (2) it neither loops (so it is not torus-like) nor contains holes (so it has one boundary) (Henle, 1979). Here it is considered that cells may be gluedlpasted together along their faces. This way it is possible to form simple cell-complexes (in 3D these would be simple points, straight lines, flat faces or volumes). In principle, a simple cell-complex may be built up from any regular polyhedron, but this discussion is here limited to tetrahedral cell-complexes since these are used in many visualisation packages and form the simplest possible mesh for a finite element approach. However, the cube is important too, since it forms the 'grid-blocks' of the popular control volume finite difference method. First consider the example, of a cell-complex set up as a division of the modelling domain into N cubes that are divided into five tetrahedra. The modelling domain is assumed to a cube too, so it is possible to assume periodicity: i.e. count equivalent nodes on two opposite boundaries only once. There will then be N 'active nodes' and T = 5N tetrahedra. Since there are four faces for each tetrahe-
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dron, shared by two, there are F =ION active faces (triangles). Thus from the Euler-PoincarC formula N - E + F - T = , where = 1 for simply connected domains [Weintraub, 19771, it follows that there are E = 6N - 1 active edges [Henle, 1979; Bossavit, 19981. In a similar way it is possible to derive a division of the cubes into six tetrahedra, leading to T = 6 N , F = 12N and E = 7N - 1. Looking at these numbers, the first thought is that numerical models based on nodes as degrees of freedom (e.g. CN-FEM) are quickest with respect to computer time and memory requirements. The second-cheapest method would be based on edges, whereas the most computationally intensive methods would be based on faces (e.g. MH-FEM). Of course, tetrahedral cell-complexes may have a much more general and 'irregular' character. For instance, they may be obtained from alpha complexes, originally developed for visualisation and pattern recognition (Gerritsen et al., 2001a, 2001b).
x
x
10.3 Process modelling The new approach to process modelling contains the concepts of (1) incidence and (2) orientation. Each edge has a 'forward direction' from which each face has a notion of 'turning clockwise', thus each tetrahedron has its own 'corkscrew rule'. Even nodes are oriented, shown by attributing a sign, + or - (Paoluzzi, 1993). Two simplices, 0 and s , can be assigned, of dimensions d and d + 1 respectively. The incidence number will be: + 1 if 0 is a face of s , or 0 otherwise. The sign, + or -, depends on whether the orientations match or not. Gathering the incidence numbers in arrays leads to three matrices: (1) the T x F matrix D , (2) the F x E matrix R and (3) the E x N matrix G (Henle, 1979; Bossavit, 1998). These incidence matrices have an appealing meaning: D , R , G are the discrete counterparts of the spatial differential operators div, rot, grad upon which the process models are based. Indeed, for topological 'balls' D R = 0 and R G = 0 , which are the discrete analogues of the well-known relations div(rot.) r O and rot(grad.) =O- . However, in contrast to the partial differential operators, the incidence matrices are metric-free, i.e. depend only on topology of the cell-complex, offering a major advantage regarding the assumed topological priority over matter. The duality of meshes also plays an important role in process modelling. For a tetrahedral primal mesh, the dual mesh is such that the barycentres of the tetrahedra of the primal mesh correspond to the nodes of the dual mesh, whereas the faces (triangles) of the primal mesh correspond with the edges of the dual mesh, and so on (Figure 10.1). More specifically, a dual edge ,&belonging to primal face f is the union of the two line segments that join the barycentre of that face with the two barycentres of the tetrahedra flanking that face. The dual mesh is not a simple mesh, since the edges are broken lines.
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Figure 10.1. The dual mesh (after Bossavit, 1998)
10.4 Paradigms The line of thought can be expressed using the following equations as paradigmatic 'first principles': div(h - grad 9)= 0 in C [ll -
rot@-' rot a) = Q in C -
PI
Both [I] and [2] describe steady single-phase fluid flow in a domain C filled with porous material. 'Div-side' equation [I] is well known in groundwater hydrology and petroleum reservoir engineering. Although 'curl-side' equation [2] is well known in the theory of electromagnetism (Maxwell presented his famous equations using a vector potential [Bossavit, 1998]), this equation is hardly known in the context of porous media flow. It is our strong conviction that [2] is extremely useful. Therefore attention will be focused on [2], while supposing that [I] and its discrete analogues are already familiar to the reader. Here = { m2.pa-'.s-)' is the mobility tensor (the intrinsic permeability divided by the viscosity), (p { pa) is the ) is the vector potential. (scalar) potential, and { mZs-' The (scalar) potential equals the fluid pressure minus the hydrostatic pressure. Consider groundwater with a density of 1 ~ ) 0 k ~ , in~ "a gravitational field of A piezometric height of I rn then corresponds to a water pressure of 1dbar = 10,OOOPa. In other words, the numerical value of the piezometric head may be equated to that of the potential, expressed in decibars. Similarly, the mobility may be equated to the hydraulic conductivity. However, for oil, which has a different density, this approach would not be valid. In contrast to what is generally believed, the vector potential also has a physical meaning. In a plane normal to its direction, the stream function will equal its abso-
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119
lute value multiplied by a metric coefficient. The flux density { rn.s-' } (i.e. the a = -h- grad cp . specific discharge) is then given by q = rot -
-
-
Equations [I] and [2] can be extended to describe steady multi-phase flow. In the numerical example that follows, attention is focused on two-phase flow (Zijl and Trykozko, 2002). Extensions to elastic geomechanics and creep of viscous rock layers may also be possible. Although [I] and [2] are mathematically equivalent, their discrete analogues are not. This gives rise to two different numerical approximation methods, which can be used to establish error bounds. Numerical examples of the error bounds are presented next, but the theory is described by Trykozko et al. (2001).
10.5 Discrete analogues of the paradigm equations 10.5.1 The curl side Assign to each face f of the tetrahedral primal mesh a flux { rn3.day-') through that face. Denote by
91 =JJfp*v,ds
q the array of face-based fluxes and con-
sider the following linear system:
Dq=O System [3] is equivalent to
[31 qf = 0 , where the summation is over the four faces
Cf
of a tetrahedron. Hence [3] expresses mass conservation for each tetrahedron. Assign a potential difference h = h 71 - d i { dbar ) along each dual edge
k.
Let h be the column array of face-based potential differences, then the system:
R~ h = 0 is equivalent to
hf = 0 , which means that the circulation of
h
[41 is zero along
the edges of a dual face, from which it follows that h = -grad cp along the edges of each dual face [Bossavit, 19981. Equations [3] and [4] are metric-free; only the topology of the mesh matters. The metric is considered by the discrete analogue of Darcy's law: q = ~ h [51 The square F x F matrix L { m3.pa-l.s-l} may then be constructed using a control volume finite difference approach (Bossavit, 1999), but in the context of finite elements L is obtained by the Galerkin approach (Bossavit, 1998):
where the mobility defined as
h_ - is a tetrahedron-wise constant. The vector function
we = wmgradwn - wngradwm
IV,is
[71
120
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Zijl, Trykozko
where edge e points from node m to node n . Scalar function W, is the well-known continuous piecewise linear 'hat' function. This is equal to 1 at node n , and to 0 at other nodes. Since the mobility & - is a symmetrical and strictly
positive definite tensor, the matrix L is also symmetric and strictly positive definite. Combining [4] and [5] using [6] leads to the algebraic system: R TL-lq=0 [81 To transform system [8] to a solvable E x E system, the relation D R = 0 is invoked. Writing this relation as D (R a) = 0 , and recalling that D q = 0 , suggests the introduction of an array a of edge-based degrees of freedom a, such that q = R a . In such a way the discrete analogue of q = rota- can be obtained. Here
l-
ae = a
-e
dh { rn3..day-' } is the tangential component of the vector potential in-
tegrated over primal edge e . Moreover, q, =
zeae , meaning that the circulation
of a .T- along the edges of a primal face is equal to the flux through that face. This finally leads to the algebraic system: R T L-I R ~ = O [91 of which the E x E matrix R~ L? R is sparse and symmetrical. System [9] is the discrete analogue of [Z]. Since in [I] and [9] there is no requirement for diva- ,physical boundary conditions can be inserted (Bossavit, 1998). The resulting system has a positive semiis definite matrix with null space containing the range of G (i.e. the solution unique up to an arbitrary function grad f ). This system can be solved using a conventional preconditioned conjugate gradient method (Bossavit, 1998; Kaasschieter, 1988; Meijerink and Van der Vorst, 1977). 10.5.2 The div side Using a similar line of reasoning, the discrete analogue of [I] yields the system of equations: G' L * G ~ * = o [lo] where p* is the array of node-based scalar potentials and matrix that belongs to the 'classical' conformal-nodal finite element method (CNFEM) (Strang and Fix, 1997; Zienkewicz and Taylor, 1967). It may be noted that G~ is the discrete analogue of the divergence in the dual mesh. In other words, qi = O , summing over the faces 1 of a dual volume. system [lo] is equivalent to Hence [lo] expresses mass conservation for each dual volume (Figure 10.1).
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Systems [9] and [lo] are complementary in the sense that system [9] gives lower bounds for the eigenvalues of the numerically approximated coarse-scale mobility, while system [lo] gives upper bounds (Trykozko et al., 2001).
10.6 Discrete flux-based process models Computational electromagnetics employ edge elements for the vector potential. With respect to porous media flow, the flux density (or friction in geotechnics) are considered to be the standard alternative to the 'classical' potentials (of pressure and piezometric head). In contrast, node elements have a long history and so are well established. Edge elements are relatively new and have not (yet) found acceptance within the earth sciences. This is the reason for developing node-based methods for describing the flux density. The simplest concept is to derive 'div-side' equations for the flux density. Taking the curl of [2], and using the relation rot(rot .) = grad(div .) - div(grad. ) yields div(h-' grad q,) + div(qi grad X1)- ai(q - grad h-') = 0 for any isotropic medium. If perfect layering is assumed, e.g., A =A(z) , an equation for q, can be found that has comparable simplicity to [I]: As before, assuming that the medium is perfectly layered and taking the
y derivatives of [I] will yield: div(h grad h,) = 0 , div(h grad h,) = 0 in C
x and
[ 1lb,cl Equations [I lb,c] are a special case of div-side equation [I], whereas [1la] is a special case of curl-side equation [2] in div-side disguise. The numerical solution of these equations leads to a complementary algebraic system and so to a posteriori error bounds. Instead of solving the relatively complicated equations [I], [2], a simpler way to solve their equivalents [ l l ] using the established node-based finite elements method has been accomplished by Nawalany (1986). In addition, [ l l ] forms the basis of a practical engineering result: the calculation of vertical flux density from solutions to the Dupuit approximation in aquifers, and subsequent upscaling to obtain aquifer transmissivity (Zijl and Nawalany, 1993). The mixed hybrid finite element method (MH-FEM) provides an alternative curl-side method that is not based on the vector potential. Using the identity R ~ ~0 D -~the discrete analogue of rot(grad . ) -0 in the dual mesh - [4] yields:
where p is the array of discrete scalar potentials p, at the dual nodes (the barycentres of the tetrahedra). In this way, equations [3] and [5] yield the mixed system (Trykozko et al., 2001):
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Left multiplying the upper equations of [13] by R~ and using [4] and [12] yields [8] and, hence, [9]. Moreover, since the null space of R~ is the range of oT, the inverse procedure, starting with [8] and ending up with [13], is also possible. Hence the two systems [9] and [13] are algebraically equivalent. Since it is relatively simple to prove the convergence of [9], the algebraic equivalence is a much simpler proof of convergence of the mixed (and mixed-hybrid, see below) method than presented in the literature (Bossavit, 1998). Since mixed system [13] has poor algebraic properties, the now classical move to the mixed-hybrid approach has been applied. Hybridisation of [13] has been described extensively in the literature (Chavent and Jaffrk, 1986; Kaasschieter and Huijben, 1992; Raviart and Thomas, 1977; Trykozko et al., 2001; Weiser and Wheeler, 1988). The procedure is only briefly summarised here. First q and h are doubled such that there are two distinct values on the sides of each face. This leads to a redefinition of q , h , L , D , R , denoted here as q' , h' , L', D', R' . This step may be considered as a domain decomposition technique for each tetrahedron. The constraint M q/ = 0 expresses the continuity of normal fluxes on all faces, where matrix M has a simple structure. Constraints are introduced using a face-based Lagrange multiplier array h. The Lagrange multiplier of each face represents the potential difference between the two adjacent dual nodes. Although the meaning of q' and h' has changed, this yields a new, but algebraically equivalent, version of [9] and [13]:
Since L' is block-diagonal, q' can be eliminated. Thus a symmetrical system is derived in terms of
p and h. Eliminating p leads to an
F x F system in terms
of just h:
M [L' - L'D'~ A'~D'L'] M~h = 0 where A = D' L' DfT(Kaasschieter and Huijben, 1992, Trykozko et al., 2001). System [15] may be considered to be a preconditioned version of the algebraically equivalent systems [9], [13] and [14]. If mobility tensor - is diagonal, the matrix
A is also diagonal. This observation leads to the famous proof that block-centred finite differences are nothing but a special case of mixed-hybrid finite elements (Weiser and Wheeler, 1988).
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10.7 Numerical example: homogenisation of flow parameters Consider an isotropic porous medium with absolute permeability k(n) = c ( X ) ~ l ~pm2 - 3 (1p,m2= 1013.250 mD), where ~ ( xy , z) = 8 ~ - ' a P ycosh2x cos2 y cosh2 z in the domain
lyl
p,
I Z I 5 y . Outside this domain the function C(X)-
IxJ 5 a ,
is continued periodically.
( a+ isinh2a) (P ++sin 2P) (y + +sinh 2y) is such that (5) = 1 and (6-') = 8-'(apy)-2p tanh a tan p tanh y . In the examples chosen,
The factor
p
=
a = y = 4 9 / 5 0 and P=49n/lOO. The water has viscosity ql = 5 xlo4 P a s = 0.5 cp, whereas the oil has viscos~ = 50 cp. The relative permeabilities are ity q 2 = 5 ~ 1 0 - Pa.s - ($)2i(i)) . The capillary 6(sl) = $ (Stc(i)- (+)2c(*)) and r2(s2)= f (s:~(~)
+ (x)
pressure is given by p(x, sl)/ po = b tan((9 - 20sl)n/ 14) d ( po = 1 kPa = 0.1450377 psi) where either b = 0 and d is constant (the no-capillary gradient case) or b = 1 and d (z) = 51(&)-'/ 2 (the saturation-dependent capillary case). The admissible interval for the water saturation is 1/10 1 sl 5 415 ; the residual oil saturation is 115 and the maximum oil saturation is 9/10. In this example the exact K is given by the diagonal matrix coarse-scale absolute permeability diag(K,, K,, K,) = diag(F,, F,, F , ) x ~ o -prn2.~a-1.s-1, ~ , Fy = 2p2cot PI@+ q)and Fz = 2y2cothy l(y + F) where Fx = 2u2cotha/(u + y) (Trykozko et al., 2001; Zijl and Trykozko, 2001). Using the finite element analogues, where the fine-scale mobility is tetrahedron-wise constant, the fine-scale mobility using the above expressions in its barycentre is assigned to each tetrahedron. In order to perform the numerical experiments, the following nine values of v have been specified: 0.0001, 0.0003, 0.001, 0.0 1, 0.1, 1, 10, 100, 300. The bisection method (Press et al., 1986) is used with termination criterion
VI
Ir,(S2)/ q(s1)- 2 yielding the coarse-scale saturations sa for each specified V from which the corresponding mobilities are calculated. To solve the saturation-dependent capillary case, the saturations obtained from the no-capillarygradient case have been chosen as initial values for the successive iteration procedure. The results obtained for the saturation-dependent capillary pressure are shown in Figures 10.2 to 10.4. Since there is now access to two algebraically different approximation methods, there is a temptation to compare their behaviour. Thus, a set of computational experiments has been performed aiming to study the two approximate solutions. Only the parameter value v = 0.1 has been chosen for this purpose. The computa-
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tions were performed for a sequence of uniformly refined meshes. The results are summarised in Figures 10.5 to 10.7. The two methods provide solutions that are very close to each other. In general, the differences in the yy component between two solutions are greater. This is due to the form of the fine-scale absolute permeability, which steeply descends to zero near the y-boundaries. The function describing the absolute fine-scale permeability is continuous, whereas the two finite-element methods use tetrahedron-wise constant fine-scale mobility values. Thus for each mesh in the sequence used, another discrete problem is derived from the continuous problem. For each problem in the sequence of discrete problems, the CN-FEM and MH-FEM solutions represent respectively the upper and lower bounds, although the CN-FEM and MH-FEM solutions presented do not bound the exact solution of the continuous problem.
Mixd-Hybrid
-b
Confoml-Nodal
I
capillaly pressure
Figure 10.2. Coarse-scale water saturation S, vs. coarse-scale capillary pressure P Water mbilities vs. saturation S 1, saturatio~dependentcapillary
water saturation
Figure 10.3. Coarse-scalewater mobility
A; vs. coarse-scale water saturation S,
Chapter 10
Numerical schemes for process simulation
Oil mobilities vs.saturation S2, saturation-dependent capinary
oil saturation
Figure 10.4. Coarse-scale oil mobility
A:j
vs. coarse-scale oil saturation S,
Capillary pressure
5000
10000
15000
20000
numberofactive nodes
Figure 10.5. Coarse-scale capillary pressure vs. number of active nodes
125
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Zijl, Trykozko
Coarse-scale saturation
,
05
number of active nodes U MH
- No-capillary - - .=- - - CN - No-capillary +MH
- Capillary -)C
CN - Capillaty
Figure 10.6. Coarse-scale water saturation vs. number of active nodes
Coarse-scale water mobilities, saturation-dependentcapillary
0
5000
10000
15000
20000
number of active nodes +Mixed-Hyblid
FEM +Conformal
Nodal FEM
Figure 10.7. Saturation-dependentcapillary: coarse-scale water mobility ber of active nodes
vs. num-
10.8 Conclusions It has been shown that tetrahedral cell-complexes are not only useful for visualisation and pattern recognition, but also for process modelling. The essential jump from cell-complex to process simulation is bridged by the application of equiva-
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lence between the three incidence matrices of a cell-complex with the three spatial differential operators from process models. Considering two mathematically equivalent process equations - the 'div-side' and the 'curl-side' equation - as 'first principles', it has been possible to derive the well-known conformal-nodal finite element method (CN-FEM), as well as a realm of algebraically equivalent complementary finite element methods that are not algebraically equivalent to CNFEM. The mixed-hybrid finite element method (MH-FEM) is the most popular complementary method, but the newer edge element method shows considerable promise and deserves further research. Arguments have been presented showing that CN-FEM combined with edge elements may be considered as a generalisation of Nawalany and Zijl's velocity-oriented finite elements. The div-side equations have been solved numerically to determine upper bounds, whilst the 'curl side' equations have been used to compute lower bounds. Homogenisation of two-phase flow parameters has been presented as a numerical example. Future research could focus on building a relatively simple, hence fast, process model for visualisation and pattern recognition. This software could be based on tetrahedral cell-complexes, readily accomplished using the incidence matrices and [ 6 ] . To make the models faster, data reduction by numerical homogenisation could be utilised.
Acknowledgements The help of Alain Bossavit (~lectricit~ de France) is gratefully acknowledged, for his inspiring enthusiasm to base mixed-hybrid finite elements on the algebraically equivalent edge elements. Bart Gerritsen (TNO-NITG) is also gratefully acknowledged, for his enthusiasm for promoting topological concepts, especially alpha-complexes, in the earth sciences. Finally, the support of the Banach Centre of Excellence (Warsaw) is gratefully acknowledged.
References Bossavit A (1998) Computational Electromagnetism: Variational Formulations, Complementarity, Edge Elements, Academic Press, San Diego Bossavit A (1999) Computational electromagnetism and geometry: building a finitedimensional "Maxwell's house", J. Japan Soc. Appl. Electromagn. and Mech., 7, 150159 (part I), 294-301 (part 2), 401-408 (part 3) Brezzi F, Fortin M (1991) Mixed and Hybrid Finite Element Methods, Springer-Verlag, New York Chavent G, JaffrC J (1986) Mathematical Models and Finite Elements for Reservoir Simulation, North Holland Publishing Comp., Amsterdam Gerritsen BHM, van der Werff K, Veltkamp RC (2001) Modelling natural objects with alpha complexes, Computer Aided Design, Special Issue on TMCE Gerritsen BHM, Zijl W, Trykozko A (2001) From alpha complexes to finite element analysis: exemplified by barchan dune avalanching, Computing and Visualisation in Science
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Henle M (1979) A Combinatorial Introduction To Topology, WH Freeman and Company, San Francisco Kaasschieter EF (1988) Preconditioned conjugate gradients for solving singular systems, Journal of Computational and Applied Mathematics, 24, 265-75 Kaasschleter EF, Huijben AJM (1992) Mixed-hybrid finite elements and streamline computations for the potential flow problem, Numerical Methods for Partial Differential Equations, 8,221-226 Meijerink JA, van der Vorst HA (1977) An iterative solution method for linear systems of which the coefficient matrix is a symmetric M-matrix, Mathematics of Computations, 31,148-162 Nawalany M (1986) FLOSA-3D - Introduction to system analysis of three-dimensional groundwater flow. TNO-NITG Report No. OS 86-07, DelftIUtrecht Paoluzzi A, Bernardini F, Cattani C, Ferrucci V (1993) Dimension-independent modelling with simplicial complexes, ACM Transactions on Graphics, 12(1), 56-102 Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1986) Numerical Recipes in FORTRAN: The Art of Scientific Computing, Cambridge University Press, New York Raviart RA, Thomas JM (1977) A mixed finite element method for 2nd order elliptic problems. In: Mathematical Aspects of the Finite Element Method, Lecture Notes in Math. 606, Springer-Verlag, New York, 292-3 15 Strang G, Fix GJ (1973) An Analysis of the Finite Element Method. Prentice-Hall, Inc., Englewood Cliffs Tanaka Y (1984; 1985) The comparison between Einstein's and Whitehead's theory of relativity, 1" and 2""itehead Relativity Conference, Claremont, Calif. URL www .asahi-net.or.ip1-sn2y-tnkhanaka-4-0. htm Trykozko A (1991) Analyse de quelques mtthodes de maillage adaptif. Note Technique, ~lectricittde France, Direction des Etudes et Recherches, Clamart, Trykozko A (1997) Model numeryczny izolacji hydraulicznej wysypisk odpad6w. Ph.D. thesis, Politechnlka Warszawska, Wydzia* Ineynierii erodowiska Trykozko A, Zijl W, Bossavit A (2001) Nodal and mixed finite elements for the numerical homogenization of 3D permeability. Computational Geosciences, 5, 61-84 Weintraub SH (1977) Differential Forms. Academic Press, San Diego Weiser A, Wheeler MF (1988) On convergence of block-centred finite differences for elliptic problems. SIAM J. Numerical Analysis, 25, 351-375 Zienkewicz OC, Taylor RL (1967) The Finite Element Method. MacGraw-Hill, London Zijl W, Nawalany M (1993) Natural Groundwater Flow. Lewis Publishers I CRC Press, Boca Raton Zijl W, Trykozko A (2001) Numerical homogenization of the absolute permeability using the conformal-nodal and the mixed-hybrid finite element method. Transport in Porous Media, 44, 33-62 Zijl W, Trykozko A (2002) Numerical homogenization of two-phase flow in porous media, Computational Geosciences (in press)
11 Regional subsurface models and their practical usage Hans-Georg ~obisch',Henning ~ombien'
' Geologisches Institut der Universtat zu Koln, Germany
Bundesanstalt fiir Geowissenschaften and Rohstoffe (NLfB), Hanover, Germany
11.I The Context Until 1998, the Geological Survey of Lower Saxony (NLfB) compiled and published regional geological maps at a scale of 1:25,000. These described the geological strata occurring at a depth of 2 m as cross sections in two directions (one of which is shown as an example in Figure 11.1) and as contours of thickness for each layer (Figure 11.2) so as to reveal the 3D subsurface condition. Digital data processing methods have subsequently enabled the NLfB construct 3D subsurface models of the ground profile down to several tens of metres based on the 1:25,000 mapping. These models reveal the stratigraphic units by integrating the 3D subsurface models within a Geographical Information System (GIs).
11.2 Modelling methods The modelling is achieved by calculating the lower bound of each layer using the elevation from a Digital Terrain Model (DTM). The cloud of points creates the base for interpolating using a triangulation that can be interactively changed. Editing is necessary to eliminate triangles that lie outside the lateral boundaries (Figure 11.3). The triangulation is then projected to a C1-surface and the elevation values stored as a grid, repeating the process for all layers to build the first model. Using this model, automatically constructed cross sections can be computed to facilitate checking concepts; cross sections are very important for detecting implausible areas. Corrections may then be applied and the model recalculated. Normally three to four iterations are needed to complete the geological model.
11.3 Boundary conditions The model consists of a number of grid files, as many as there are geological layers plus a grid for the digital elevation model of the land surface. A grid for the M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 129–134, 2003. c Springer-Verlag Berlin Heidelberg 2003
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water table as well as grids for other planes, e.g, faults, may supplement these. A separate file defines the succession of grid files, i.e. the succession of geological layers. Attribute data, for example stratigraphic information, lithological descriptions, geotechnical and hydrogeological parameter values, can be placed in tables and linked to the spatial data. Such a model of geological units permits a variety of applications:
0
Prediction of ground profile prior to drilling (Figure 11.4) Preparation of cross sections between any selected points, straight or zigzag (Figure 11.5) Mapping the distribution of any given geological formation, its thickness or depth (Figure 11.6) Portrayal of aquifers together with any cover layer which might protect the aquifer from pollution (Figure 11.7) Alternative portrayals of the geology (Figure 1 1 . Q for instance simultaneous plotting of contour lines to delineate the morphology of the older subsurface bedrock; colours can indicate the different types of sediment Graphical presentations of selected aspects, from any perspective, helpful for those unfamiliar with national cartographic conventions (Figure 11.9).
The users of subsurface models are more interested in the analytical results than in the internal structure of the data or the modelling process itself. The subsurface models can be explored using the VirGIL system, a software product especially developed for regional geological applications. The VirGIL system is an extension to the ArcView GIs, utilising the components of ArcView for graphical presentation and processing. The outcome is in the form of ArcView themes; nongeological users readily accept such models.
11.4 Outlook Dividing the modelling process into two operations - constructing the map and building the digital model - has enabled correction of the geological base information. The digital model is able to support fieldwork and assist the evaluation of borehole logs as well as deliver digital models of even higher quality.
Figure 11.1. One cross section as an example (Nordhorn area)
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Figure 11.2. Geological map 2 m beneath the land surface in the Diepenau area
- ,n1x
Figure 11.3. Triangulation of all significant points collected from cross sections and lateral boundaries changed interactively (loamy glacial till, Diepenau)
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Figure 11.4. Predicted lithology of planned boreholes in the Diepenau area
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Figure 11.5. Cross-section in the Diepenau area: Upper: Quaternary geology; Lower: hy drogeology
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Figure 11.6. Thickness of fluviatile gravel and sand deposits in the Diepenau area
Figure 11.7. Thickness of an aquifer (red) and its cover of loamy glacial till (hatching) in the Diepenau area
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Figure 11.8. The Mesozoic/Quatemary interface (contour lines) and the overlying unconsolidated Quaternary sediments in the Diepenau area
Figure 11.9. Perspective view from the south-west of the Diepenau area [Top shows the geological map of the land surface calculated from the model and projected onto the digital elevation model; walls show the computed cross sections]
12 Slope stability estimation using GIs Maria Ferentinou, Michael Sakellariou National Technical University of Athens, Greece
12.1 The Context Geographical Information Systems (GIs) and Decision Support Systems (DSS) offer help with natural hazard and risk zonation mapping (Burrough and McDonnell, 1998). GIs is able to: Organize spatial data, Visualise complex spatial relationships of a particular location, make a Spatial Query concerning the characteristics of a particular location, Combine datasets from different sources, Analyse the data (inferring meaning) and make Predictions by combining data layers according to purposely embodied rules (Bonham-Carter, 1997). Hazard mapping related to slope stability is considered to assess the potential contribution by GIs. Landslides are the result of complex processes controlled by many different factors: lithology, geological structure, geomorphological character, land use, groundwater, climate and seismic activity. Climate and seismic activity change periodically, and thus are the two main triggering factors likely to initiate a landslide. Information on lithology, structure and geomorphology can be included in the mapping process, and can form a hazard map since they are not time dependant factors. Landsliding is the result of rapid change in position of a mass, either natural or man-made, in response to driving forces such as gravity and seismicity. The influence of climate on landslide processes can be considered either as a weathering factor, acting slowly, or rapidly as rainfall, snow or frost. In this paper rainfall is implemented in the model using the pore water pressure ratio (r,).
12.2 The relevance of GIs to slope stability One of the main advantages offered by GIs is the possibility of creating hazard occurrence models, permitting evaluation of results and adjusting the input variables. The user can achieve an optimum model by a process of trial and error (Deketh et al., 1997). The large variability of geotechnical properties (cohesion, angle of internal friction), and their host geological units (location) and groundwater condition, is inconsistent with the homogeneity of data required by deterministic models; averaged values are generally sought. M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 135–140, 2003. c Springer-Verlag Berlin Heidelberg 2003
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According to the UNESCO Working Party on the World Landslide Inventory (Popescu, 1996), landslide causal factors may be categorised as: Ground conditions Geomorphological processes Physical processes Man-made processes The hardest problem is to assign the relative weight by which each may be considered to affect the whole system. The GIs model can consider two categories of factors: Those derived from a DEM (slope angle, height, elevation, illumination, aspect) Those derived from published data, referred to as qualitative information.
12.3 Development of a landslide hazard assessment tool The study employed a relational database management system implemented within ArcIInfo. Methods for calculating slope stability are generally based on the calculation of a Factor of Safety, a function of both geometry of the slope and the geotechnical parameters. The GIs has been developed as a tool to extract relevant data from ArcIInfo for each slope unit, as a facet of the triangulated irregular network built from the elevation data (Figure 12.1). The method is based on a simple approximation proposed by Sah et al. (1994), utilising a circular failure mechanism:
F = 2.27 . Where
c .cosecy
Y .H
+ 1.54. (1- ru) .cot I,Y
tan 4,
: unit weight of soils c: cohesion : internal friction angle *,: slope angle H: slope height ru:pore water pressure ratio F: safety factor.
A dynamic tool has been created which enables the user to alter the values of the parameters being used each time a new model is produced. The user can select the values which describe each property according to a desired combination, giving an output as a landslide hazard zonation. Such an approach can incorporate timedependant changes in value of geotechnical parameters provided a cost effective procedure to monitor them is available.
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The methodology has been evaluated against a neural network constructed from 46 case histories of slopes analysed for circular failure by Sakellariou et al. (1996) (Figure 12.2). The formula has been implemented in a routine in the ArcAnfo application using AML (ArcIInfo Macro Language) in order to calculate the Factor of Safety for each slope.
Figure 12.1. Hillshaded map
- - - safety factor simple approximatio n
Figure 12.2. Factor of Safety estimation
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12.4 Case Study: Diakopto, Greece The study area covers approximately 48 lun2,situated in the northern part of the Peloponese, 5 km east of the city of Aegio. The valleys of the Vouraikos and Kerinitis rivers cut the area, the former connecting the towns of Diakopto and Kalabryta. The railway connecting the two towns has been affected by rock falls: debris flows and shallow landslides have been recorded along the road nearby. Weakly cemented mark and sandstones of the Miocene overlie the Mesozoic sequence. The geological setting in which the severest slope instability occurs consists of alternating layers of marls, mudrocks and cherts, overlying thinly bedded limestone. The stratigraphy has frequently been modified by tectonism (Stournaras et al., 1998). A series of thematic maps has been created taking into consideration lithology, slope angle, elevation, aspect, and rainfall so as to evaluate the hazard. Geometrical factors can be obtained from a digital elevation model (elevation, slope angle, aspect). The geotechnical factors (cohesion, pore water pressure, unit weight, angle of internal friction) are obtained from published sources (Table 12.1). Table 12.1. Geotechnical parameters. Geological Formations Loose deposits with dominance of fine grains.(Q.f-1) Loose Deposits with dominance of coarse grains.(Q.c-1) Loose deposits with dominance of mixed facies.(Q.f,c-1) Coarse grained formations with cohesion. (Q.c-cm) Formations of mixed facies with cohesion. PlioPleistocene Deposits (Q.f,c-cm) Coarse grained sediment (P1.c) Mainly fine-grained sediments. (P1.f-c) ~ r e n e o ~ e nformations ic (pNf)
I
Cohesion
I
I
Unit Internal Weight friction y K N / ~ ~cp degrees 15.7-23.5 8"-35" 19.6-23.5 25"-50" 18.1-20.6 10"-40" 20.6-27.5 35O-65" 23.0-25.0 3 8O-46"
Rainfall precipitation in the area is high, and seasonal. Annual rainfall ranges from 800 to 2400 mm; 50 % of the total annual rainfall is concentrated during the winter months (Koukis, 1996). In addition, the surface run-off has a capacity to transport large quantities of material from the catchment. During the dry periods, the ground becomes desiccated and the surface becomes loosened, and prone to erosion. Furthermore the ground desiccates, so that rainwater readily percolates through the cracks, building-up the pore pressure within (Stournaras et al., 1988).
12.5 Landslide hazard zonation The hazard was defined by Varnes (1984) as the probability of occurrence of a potentially damaging phenomenon within a specified period of time and within a given area. Thus zonation through subdivision of an area into homogeneous do-
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mains according to the degree of actual or potential hazard can utilise the landslide model, providing a quantitative estimate of potential landslide occurrence. The parameters selected for adjustment were: slope angle and slope height (both derived directly from the DEM), and the pore water pressure ratio. The slope height was at first estimated at 50 m for all classes of slope angle, and pore pressure ratio taken as rU= 0.00; there were very few unstable slopes (Figure 12.3). If the value of ruwas raised to 0.50, many slopes would potentially fail (Figure 12.4). The modelling was repeated for a variety of typical slope angles and heights (20-30" slope 90 m height, 30-40" 100 m, 40-50" 150 m, 50-60" 80 m, 60-70" 60 m). Pore water pressure ratio was varied between 0-0.5.
12.6 Conclusions In assessing the hazard vulnerability of an area, considerable uncertainty is present. Landslides represent complex multivariable processes. Models are needed to evaluate their consequences, and may be portrayed in the form of hazard maps utilising GIs to create a landslide hazard zonation. By applying this methodology in the Diakopto study area, it was observed that wet slopes of average height have a high risk of instability, consistent with field observations.
References Bonham-Carter GF (1997) Geographical Information Systems for Geoscientists: Modelling with GIs, Pergamon Press, 398 pp. Burrough PA, McDonnel RA (1998) Principles of GIs. University Press, Oxford, 124 pp. Deketh HJR, Rengers N (1997) The Role of engineering Geology in Natural Hazard Reduction for Sustainable Development. Special Lecture International Symposium of the IAEG: Engineering Geology and the Environment. Balkema, Rotterdam, 4 ESRI (1995)Understanding GIs: The ArcIInfo method. ESRI Koukis G, Rozos D, Hadginakos I (1997) Relations between rainfall and landslides in formations of Achaia county. International Symposium of the IAEG: Engineering Geology and the Environment, Balkema, Rotterdam Koukis G, Pyrgiotis L, Rozos D (1997) Landslide phenomena and instability analysis related with the construction of the Diakopto road deviation. International Symposium of Engineering Geology and the Environment, Balkema, Rotterdam, 1,783 Peuker TK (1977) Data Structures for digital terrain models: &scussion and comparison. In: Proc. Advanced Study Symp on Topological Data Structures for Geographic Information Systems, Harvard University, Cambridge, Massachusetts Popescu ME (1996) From Landslide causes to Landslide Remediation. Special Lecture at the Seventh International Symposium on Landslides, Trondheim Rozos D (1991) Technicogeologic conditions in the Achaia County: Geomechanic Characters. Ph.D. thesis Geotechnical Investigations n. 16, IGME, Athens [unpublished] Sah NK, Sheorey PR, Upadhyana LN (1994) Maximum Likelihood Estimation of slope stability. Int. Jn. of Rock Mech. Min. Sci and Geomech. Abstr., 31(1), 47-53
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Sakellariou M, Helias P (1997) Investigation of the possibility of applying neural networks in the estimation of slope stability. Proceedings of the 3'* Hellenic Conference on Geotechnical Engineering, Patra, 2,269-276. [in Greek] Sakellariou M, Ferentinou M (2001) GIs based estimation of slope stability. Natural Hazard Review, ASCE, 2(1) Terzaghi K (1950) Mechanics of landslides. Geol Soc America Berkey Volume, 83-124 Turner AK, Schuster RL (1996) Landslides Investigation and Mitigation. Special Report 247, Transportation Research Board, National Academy of Sciences Stournaras G, Tsimpidis G, Tsoumanis P, Yannatos G, Guillande R (1998) Instability phenomena in the Neogene Deposits of Peloponnese. Bulletin of IAEG, 5 7 , 6 5 4 8 Varnes DJ (1984) Landslide Hazard Zonation: a renew of principles and practice. Commision on Landslides of the IAEG, UNESCO, Natural Hazards No 3
Figure 123. Landslide hazard zonation map; stable areas prevail
Figure 12.4. Landslide hazard zonation map; medium to high hazard areas prevail
13 Using 2D GIs for the 3D reconstruction of the Corno Zuccone sackung, Val Taleggio (Italy) Andrea zanchi1j2,Simone sterlacchini2, Crosta G.B.' UniversitA degli studi di Milano-Bicocca, Milan, Italy CNR, Milan, Italy
13.1 Context The development of specialist software for 3D visualisation (e.g. Lynx, gocad, Earth Vision) permits reconstruction of complex geological bodies. Examples have been published concerning studies based on boreholes and geophysics, but few have concerned mapped structural bodies (de Kemp, 2000). The systematic use of Geographical Information Systems (GIs) facilitates the management and subsequent analysis and use of geological information contained as cartographic representations in geological maps (Ramsay and Hubert, 1987; Powell, 1992).
13.2 The Corno Zuccone landslide The Corno Zuccone slide is a deep-seated gravitational slide developed in the sedimentary cover of the Southern Alps (Figures 13.1 and 13.3) along the western part of the Taleggio valley, Bergamo, Italy (Crosta et al., 1999; Zanchi and Stelluti, 2000). This structural sector of the Alpine belt consists of a complex southerly converging stack of thrust sheets comprising a Middle Triassic to Jurassic sedimentary succession. Early to Middle Pleistocene fluvial deposits cover the lower part of the area, indicating strong erosion and dissection of the topography. The most important gravitational structures consist of E-W and ENE-WSW trending high-angle slip planes, developed within the uppermost part of the Corno Zaccone klippe, suggesting a downward motion of the slipped mass of at least 100-150 m. Geomorphological analysis suggests that the lower (Taleggio) unit, consisting of shales and marly limestones of the Argillite di Riva di Solto Formation is affected by gravitational deformation, involving an area of some 5 km2. On-going activity of the lower part of the landslide is suggested by building damage (e.g. sliding at Enna bridge in the village of Lavina). M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 141–150, 2003. c Springer-Verlag Berlin Heidelberg 2003
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13.3 Using 2D GIs for 3D modelling in gocad The specialist sofiware gocad has been developed for the creation and management of 3D objects from 2D features such as points, lines and grids. gocad objects are defined by a finite number of discrete points in 3D space. Their topology is modelled through node connections to which are attributed physical properties (Figure 13.2). Surfaces consist of triangles constructed by interpolation using the Discrete Smooth Interpolator developed by Mallet (1989).
13.4 Reconstructing the Corno Zuccone landslide The construction of a set of surfaces to define the structure of the local geology is a necessary precursor to reconstructing the geometrical characters of the slide. Four main sources of information have been used to develop the 3D structural model: topographic data represented by contour lines geological and tectonic boundaries consisting of 2D linear elements structural measurements comprising attitude of bedding, thrusts, strikeslip faults, and gravitational failure surfaces geological cross-sections reconstructed from an analysis of the surface geological mapping. The ArcIInfo GIs used for this study essentially worked only with 2D data formats. In order to construct a 3D model, elevation values need to be assigned to each vertex, an operation that can be conducted using gocad. This requires the construction of a digital elevation model (DEM), obtained by interpolating the 10 m contour lines derived from the 1:10,000 topographic map of the area, employing a 2.5 m pixel side length and a vertical thickness of 0.01 m. The resulting set of points was thinned to a pixel size of 12.5 m before being imported into gocad. The reconstruction of the topographic surface could then be conducted by interpolation of the point set. Properties such as elevation could then be transferred and linked to the 2D geological boundaries previously imported into gocad as 'standard' elevation values, subsequently transformed into 3D splines. Further information for constructing the 3D model could then be added from geological cross-sections located within the 3D regular grid as voxels (Figure 13.4). Structural measurements could then be transformed into down-dip plunge lines of appropriate length (50-100 m) utilised as linear constrains (de Kemp, 1999). Construction of the hidden surfaces such as slide planes and faults, could then be represented as closed surfaces encompassing rigid blocks separated by tectonic elements (Figure 13.5). This operation was realised by the interpolation of linear elements such as vectors derived from mapped data.
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In order to reconstruct the fault grid affecting the Corno Zuccone klippe, crosscutting relationships among the different tectonic structures had first to be established, starting with a perspective view (Figure 13.6a). Based on field relationships, gravitational surfaces are likely to be the most recent. A relative chronology can then be used to reconstruct the fault sets, starting with the youngest. Gravitational structures would be expected to be listric, progressively flattening (Figure 13.6b). This is supported by the superficial trace of the structures and by the mechanical properties of the displaced rock bodies. Strike-slip faults would be expected to have a vertical orientation and thus be distinguished in their outcrop pattern from low-angle thrust or high angle normal faults (Figure 13.6~). The application of this approach to the Corno Zuccone landslide, using both geometrical and numerical modelling, suggest the possibility of a sackung-type deformation for the Corno Zuccone deep-seated gravitational slide, partially due to sagging of the klippe (Zischinsky, 1966, 1969; Radbruch-Hall, 1978; McCalpin and Irvine, 1995). In order to check the mechanism of formation of the sackung, a basal surface has been reconstructed using surface elements such as the trace of the main scarp and part of the drainage pattern. The reconstructed planes are likely to be underlain by a basal 'detachment' surface, suggested by geomorphological indications (Figure 13.6d). Such 'construction and disruption' of the sackung can be undertaken with gocad (Figure 13.7). In order to check in 3D the consistency of the 2D sections used to reconstruct the sackung, a similar operation was followed to restore them to their pre-sliding position (Figure 133 ) .
13.5 Discussion The procedure has demonstrated that 3D modelling can examine and check for geometrical consistency, even when only surface data is available. The use of 3D visualisation software strongly enhances the understanding of geological structure and the recognition of solutions that are not plausible. 3D surfaces have been realised using gocad through the reconstruction of topographic surfaces interpolated from point sets extracted from a DEM and subsequent attribution of elevation properties to 2D lines (Mallet, 1990). Thus a 3D geological model of the Corno Zuccone massif has been constructed, talung into account surface geology and subsurface interpretations obtained from cross-sections. Based on the geometrical reconstruction, it has been possible to compute isopachs of the Corno Zuccone slide and the potential for parameterisation as a basis for geotechnically modelling the slope stability. Crosta et al. (1999) developed numerical models with UDEC, a distinct element code (ITASCA, 2000). Their first model was based on a triangular section of the Corno Zuccone klippe, and the second considered a trapezoidal section. The latest work suggests that only the second situation is plausible, thus constraining the possible failure mechanisms.
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Furthermore, the displacement of the klippe suggests that deformation cannot be accommodated by plastic deformation of the lower unit alone, and that a basal shear plane must also be active. The strong increment of y-displacement below the klippe suggests the occurrence of a shear plane, and thus is almost coincident with the main shear plane obtained using geometrical reconstruction techniques. The triggering factor for the sackung may be related to a rapid deepening of the valley bottom suggested by the recent excavation of the Enna gorge in the eastern part of the Taleggio Valley.
References Crosta G, Jadoul F, Sterlacchini S, Zanchi A (1999) Activation of Deep Seated Slope Gravitational Deformations in complex tectonic settings: the Corno Zuccone sackung, Val Taleggio (Italy). Studi Geografici e Geologici in onore di Severino Belloni, a cura di G. Orombelli, 303-323 de Kemp EA (1999) Visualisation of Complex Geological Structures using 3D Bezier Construction Tools. Computer and Geosciences, 25(5), 581-597 de Kemp EA (2000) 3D visualization of structural field data: examples from the Archean Caopatina Formation, Abitibi greenstone belt, Qebec, Canada. Computer and Geosciences, 26 ( 9 , 509-530 ITASCA (2000) UDEC (Universal Distinct Element Code). User's manual. ITASCA consulting group. Minneapolis, MN Mallet JL (1989) Discrete smooth interpolation in geometric modelling. Association on Computing machines. Transactions on graphics, 8(2), 121-144 Mallet JL (1997) Discrete modelling for natural objects. Mathematical Geology, 29(2), 199-219 McCalpin JP, Irvine JR (1995) Sackungen at the Aspen Highlands Ski Area, Pitkin County, Colorado. Environmental and Engineering Geoscience, 1(3), 277-290 Powell D (1992) Interpretation of geological structures through maps. Longman Group UK, 176 pp. Radbruch-Hall D (1978) Gravitational creep of rock masses on slopes. Chap. 17 In: Voight B (ed) Rockslides and Avalanches Natural Phenomenal. Developments in Geotechnical Engineering, 14, Elsevier Science Publishing Co., Amsterdam, 608-657 Ramsay JG, Hubert MI (1987) The techniques of modern structural geology. Volume 2: Folds and Fractures. Academic Press 309-700 Zanchi A, Stelluti G (2000) From geographic information systems to 3D geological modelling with gocad. Preliminary data for the cinematic reconstruction of the Corno Zuccone deep seated slope gravitational deformation, Val Taleggio, Italy. gocad user meeting, Houston, 3/10/200, 15 pp. with 14 figs. (CD-ROM) Zischinsky U (1966) Movement of unstable valley sides. Gesellschaft der Geologic und Bergbaustudenten, Mitteilungen, 17, 127-1 68 (in German) Zischinsky U (1969) Uber Sackungen. Rock Mechanics, 1(1), 30-52
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Figure 13.1. Shaded relief map of Northern Italy showing location of study area
5 TOPOGRAPEW 1:lO.OOO )
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in n voxot
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3D MODEL (volume, kinematics) Figure 13.2. Flow diagram relating GIs and gocad through 3D modelling
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Figure 13.3. Geological map of the western Taleggio. Inset: tectonic structure
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, I
Figure 13.4. 3D modelling with gocad showing construction of geological crosssections in the context of a voxel structure
-
,
step 4
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Figure 13.5. 3D modelling with gocad showing construction of complex geological bodies (closed surfaces) through the generation of surfaces obtained from linear elements
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Figure 13.6.3D models for Corno Zuccone (a) Perspective view of the surface geology of the area showing the Zuccone klippe
(b) The main structural elements of the area: 1) Valle del Chignolo left lateral strike-slip fault, 2) Valle del19Acquaright lateral strike-slip fault, 3) basal thrust surfaces of the CZ, 4) topographic surface of part of the top of the CZ klippe
(c) Some of the main gravitational sliding surfaces displacing blocks of the Corno Zuccone klippe. Note the fine triangular network of surface 1 created by gocad
(d) The Corno Zuccone klippe and the basal surface of the deep slide.
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Figure 13.7. 3D retrodeformation of the klippe of Como Zuccone: 1) lateral view; 2) frontal view; 3) bottom view of the basal thrust surface. a) pre-sliding situation; b) postsliding situation.
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Figure 13.8. Reconstructionof the sackung. Contour lines every 25 m
14 Sedimentation modelling: data analysis in Rotterdam Harbour Robert Vuurens, Pieter Maurenbrecher Delft University of Technology, The Netherlands
14.1 Introduction Sediment accumulation within ports and harbours requires dredging to maintain sufficient draft for shipping. Design of the dredging operation requires knowledge of the amount and composition (sand-sludge ratio). Sedimentation results from several geological processes. Harbours naturally form effective sediment traps. Some areas of Rotterdam harbour derive relatively clean sand from the North Sea through tidal action, whereas other areas accumulate contaminated clay transported by the River Rhine. Parameters that quantify sedimentation include: the rate of discharge from the Rhine, the physical and chemical composition of the sediment, and the flux. In order to manage the port, these various processes have to be considered, the goal being to predict the rate and location of sedimentation within the harbour basins.
14.2 Sedimentary environments The port of Rotterdam is located at the confluence of two major river systems as they enter the North Sea: the Rhine and the Meuse. The sediment flux from the Rhine is about five times that from the Meuse, the former discharging 2500 m3/s compared to the Meuse discharging 200 m3/s. These rivers mainly carry finegrained material in suspended state, or by bottom transport. The North Sea is also a major source of sediment, consisting mainly of sand and silt. These coarser sediments mainly originate from storm erosion of coastal cliffs in northern France, carried towards the Dutch coast by the northerly longshore drift. Before reaching the Maasmond area (which acts as a major sediment trap), the sediment is likely to have been deposited and eroded several times. The Western Scheldt, Eastern Scheldt and the Haringvliet outlet areas have extensive sandbar systems which act as buffer zones trapping and eroding the sediments as they move northwards (Figure 14.1). Currents within Rotterdam Harbour are influenced by river and tidal flow. The difference between high and low tide is approximately 2 metres. The turbidity M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 151–160, 2003. c Springer-Verlag Berlin Heidelberg 2003
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maximum is located a little west of Botlekhaven (Figure 14.2), forming a major transition from sedimentation predominantly originating from the North Sea from that which is mainly fluvial.
Southern North Sea basin
''1
/' /
Sediment from Rhine river/,
Figure 14.1. Sediment transport north towards the Dutch coast
14.3 Harbour study areas Three areas within the port of Rotterdam were selected in consultation with the Rotterdam Municipal Port Management and the Ministry of Transport, Public Works and Water Management. Sedimentation is believed to be influenced predominantly by tidal flow (i.e. the North Sea), by both tidal flow and river flow, and by predominantly river flow. The three study area locations shown in Figure 14.2 are:
Maasmond An area in the western part of the harbour, mainly influenced by the sea 0
Botlekhaven An area in the central part of the harbour which is influenced by both the river and the sea.
N Maasmond
Waalhaven Botlek
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gathered covers a 10-year period from 1991-2000 at intervals of a few weeks to several months (Table 14.1). Table 14.1. Measured variables.
14.5 Data-driven modelling The general approach taken for prediction has been to construct a data-driven model as depicted in Figure 14.3. The GIs is used for visualising the data distribution and performs spatial analysis. The volume has been calculated by taking the difference between two time intervals projected onto a regular grid. It is ultimately upon volume estimates that the artificial neural network will be trained. Figure 14.4 shows a difference map of one time interval for the 'real' deposited volume. An Artificial Neural Network (ANN) has then been trained to assist identification of input parameters significantly influencing the sedimentation. The network is trained using a training set, tested using a test set and validated using a validation set, each set being unique. The data were compiled from five different sources, each organisation having its own way of collating and storing the data, as well as geo-spatially referencing it. However, the data needs to be matched and integrated, so achieving a comparable resolution in space and time.
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+
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Sedimentation map
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t
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I
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IF e= minimal v
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Figure 14.3. Flow diagram for the modelling approach Difference map Maasmond 1995 period 2
'I
Hoekvan Holland
Figure 14.4. Map showing the change in water depth for a specific time interval in the Maasmond area, the tone indicating the degree of erosion or accumulation
The sedimentation can be forecast once every 2 weeks, limited by the sampling frequency of the bathymetric surveys. The optimum sampling frequency is proba-
bly more often than two weeks, but not economically justifiable for the purposes of the dredging programme. More important is that the sampled parameter be based on a sufficient measurement population. The data contains inconsistencies resulting from differences over the decade of acquisition, including data loss and changes in equipment. In the early 1990's little data was stored digitally even though it had been acquired in digital format. Increased computer capacity towards the end of the decade now permits better data handling and storage, better documentation (i.e. metadata). The diversity of project objectives has resulted in many different measurement locations. For instance, wind and wave measurements have been taken at one location near the study area, whereas river discharge and suspended sediment concentration were measured at another location, yet both sets of measurement provide data relevant to the sedimentation processes. The quality of the data is also variable. Precision of the measurement devices varies, and sometimes pre-processing has had to be performed.
14.6 Parameter evaluation Sediment transport and depositional processes are influenced by many parameters. Their identification is the first goal for a consideration of harbour siltation. The linking of ANN to the GIs has enabled the following parameters to be recognised as having influence on the sedimentation process. Those directly influencing the process (independent parameters) have been listed separately from those indirectly influencing the process (semi-dependent parameters). Independent parameters: Geography and bathymetry 0
Tidal currents River discharge Wind-speed and wind-direction
Semi-dependent parameters: Wave energy Surge Chloride content Suspended sediment concentration Chlorophyll content Sediment composition
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Other parameters considered: Historical events (deepening of channels, construction of artificial land) Ship movements Distance to shore Distance to shipping lane For each of these parameters, data had been collected over the decade 1991-2000. The next step was to identify the most important parameters influencing the sedimentation process in the research area. The most influential parameters have been identified in three ways: 1. Correlation analysis: calculating the correlation coefficient between sediment volumes and each of the other parameters, especially the time lag between different events (Table 14.2). 2. Principal component analysis: A factor extraction method for linear combinations of variables where the first component has the maximum variance. 3. Parameter ranking opinion poll (PROP): Several experts familiar with sedimentation processes examine the list of parameters and provide their 'opinion' as to which parameters should be related to each other, to what degree and which parameter would dominate. The degree of relationship and dominance from the group can be quantified numerically as a 'relationmatrix' (Table 14.3 gives the results of one such poll). At first glance the correlation coefficients shown in Table 14.2 appear rather low. However, many of the parameters are correlated. Furthermore, the correlation coefficient is a measure only for linear dependency. Table 14.2. Correlation coefficients for parameters likely to influence sedimentation. On the right, weights of the parameters having the first two principal components are indicated.
A total of 5 principal components were extracted from twelve parameters, the first two together account for 55% of the variance in the data set. Some of the parame-
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ters are correlated and ideally one should only consider parameters that are independent of each other. Since the analysis is used just for indicative purposes, the values should be treated with some caution. On the basis of the correlation analysis, PCA and PROP, preliminary model parameters could be selected, as shown below. These can then be linked to the sedimentation volume as an output parameter. Weekly variance of windspeed Wind direction Weekly averaged river discharge Weekly variance of surge Weekly averaged wave energy Wave direction Weekly averaged 1 interpolated suspended sediment concentration Weekly averaged I interpolated chloride content Weekly averaged I interpolated chlorophyll concentration The historical data has been interpreted as shown in Figure 14.8. The selected data format was ASCII in order to permit exchange of text between databases. The files were screened for obvious erroneous measurements and missing values. Metadata proved very important for permitting an overview and quick referencing of information, including the source, type of data, parameter, time interval, missing value indicator, processing, remarks, name of processor and the date.
14.7 Conclusions The primary objective was to establish a protocol that could predict volumes expected for dredging maintenance. This was to be based on trends recorded during the previous ten years together with expert knowledge. The end users of the model are the Rotterdam Harbour Authority and the Ministry of Transport, Public Works and Water Management. The main purpose is to predict sediment volume accumulation for planning dredging activities. Procedures have been developed to examine existing 'second-hand' data to determine those parameters most influential on the sedimentation process, assisted by linking ANN to the GIs.
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Table 14.3. Example of a PROP (Parameter Ranking Opinion Poll). The lower half of matrix stores the expert's anticipated degree of correlation. The upper half of the matrix indicates the dominance of one parameter over the other.
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L 4I , Collected data
u
Format change
Original data
Data screening and description
Data separation
Gridding information
Unification information
Figure 14.8. Process of data handling
Acknowledgements The Port of Rotterdam data set and the analyses of this data has been carried within the Delft Cluster project called 'A sedimentation model for the Port of Rotterdam'. Participants in the Rotterdam modelling project are: the Port of Rotterdam, Rijkswaterstaat directorates North Sea and South Holland (Department of Public Works and Water Management), Nottingham Trent University, and the Delft Cluster institutes: TU Delft, ME, Delft Hydraulics and GeoDelft.
Reference Dreumel PF van (1995) Slib- en zandbeweging in het noordelijk Deltabekken in de periode 1982-1992, Rotterdam, Ministerie van Verkeer en Waterstaat, Rijkswaterstaat DZH
15 Fluid flow in fractured formations Christos ~ s a k i r o ~ l o uMaria ', ~heodoro~oulou~, Vagelis ~aroutsos'
' Institute of Chemical Engineering and High Temperature Chemical Processes, Foundation for Research and Technology, Hellas, Greece Department of Pharmacy, University of Patras, Greece
15.1 Introduction Fractures are widespread in the ground. Their morphology affects substantially the transport of non-aqueous phase liquid (NAPL) pollutants within the groundwater regime (Sahimi, 1995). Past work has focused on the development of numerical simulators that solve the macroscopic transport equations in fractured reservoirs (Choi et al., 1997; Gerke and van Genuchten, 1993). Little progress has yet been made on elucidating the interactive effects of single fracture morphology and fluid rheology on the flow through single fractures (Federico, 1998; Fourar et al., 1993; Rossen and Kumar, 1992; Zimmerman and Bodvarsson, 1996). The most widely used flow model for a rock fracture is the parallel plate model. This is the only fracture model for which an exact calculation of the hydraulic conductivity is possible and yields the well-known 'cubic-law' (Adler and Thovert, 1999). However, fractures have rough walls and variable apertures with extensive contact between the opposing faces (Zimmerman and Bodvarsson, 1996). Fluid flowing in a variable aperture fracture will tend to follow paths of least hydraulic resistance and they will depart from the rectilinear streamlines of the parallel plate model (Silliman, 1989). The techniques of 3D fracture reconstruction from correlation functions of 2D images can be used to characterise single fractures (Adler and Thovert, 1999). However, the straightforward simulation of fluid flow in such structures requires a substantial amount of computational effort (Sahimi, 1995). The representation of fractures with pore network models, though less accurate, allows the mathematical treatment and fast numerical simulation of fluid flow in single fractures (Klint and Tsakiroglou, 2000; Rossen and Kumar, 1992). Recent optical studies of 2D SEM images of fractures have revealed that a single fracture resembles with a disordered two-dimensional network of elliptical channels (pores). The majority of organic liquid pollutants (NAPLs) that may contaminate the ground (e.g. mixtures of hydrocarbons, crude oil, asphalt, lubricating engine oils, polymer leachates) exhibit varying degrees of deviation from the linear flow behaviour of Newtonian fluids (Rossen and Kumar, 1992; Wardaugh et al., 1988). Additional non-linearities are thus introduced to the micro-flow mechanisms. The fluid flow through pores becomes complex even for simple geometries (Ikoku and M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 161–172, 2003. c Springer-Verlag Berlin Heidelberg 2003
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Ramey, 1979; Kadet, 2000), but the conventional Darcy's law would no longer be valid (Sahimi, 1993; Shah and Yortsos, 1995). There is a need for the production of new phenomenological equations relating the superficial flow velocity with the pressure gradient along the porous medium (Sahimi, 1996). With the aid of critical path analysis of percolation theory and volume averaging, several non-Darcian equations have been developed about the single-phase flow of power law fluids in homogeneous porous media (Sahimi, 1993; Shah and Yortsos, 1995). Unfortunately, the shear stress-shear rate relationships of most organic fluids rarely fit with simple power law models (Al-Zahrani and Al-Fariss, 1998; Wardaugh et al., 1988). Macroscopic momentum balances of the flow of generalised non-Newtonian fluids through porous media have been formulated by using volume averaging techniques (Liu and Masliyah, 1999), and corresponding phenomenological models have been derived for the laminar flow of Herschel-Bulkley and Meter fluids in packed beds (Liu and Masliyah, 1998). Although the role of the macromolecules on the flow of polymer solutions in porous media has extensively been studied (Sorbie, 1990; Sorbie and Huang, 1991), few attempts have ever been made to analyse fluid memory visco-elastic effects (Bird et al., 1977; Macosko, 1994) on polymer flow through pores (Souvaliotis and Beris, 1992). The aim of the present work has been the development of a non-Darcian phenomenological model for the 1-phase flow of inelastic and time-independent nonNewtonian fluid through a single fracture. This has been tackled using artificial glass-etched single fractures by combining flow simulations in pore networks with effective medium approximation (EMA).
15.2 Laboratory experiments Ozokerite (natural mineral wax) dispersions in paraffin oil were selected as synthetic NAPLs because of their shear-thinning behaviour and stability. The shear stress-shear rate relationships were measured by using a Dynamic Stress Rheometer (Rheometric Scientific, SR-200). All measurements were performed on a cone and plate geometry by using the steady stress sweep method. The rheological properties of synthetic NAPLs were very sensitive to ozokerite concentration (Figure 15.1) and hence they were able to be controlled easily by varying this parameter. The classical power law and generalised Herschel-Bulkley models (Bird et al., 1977; Liu and Masliyah, 1998) were found inadequate for the description of the rheology of synthetic NAPLs over the entire range of ozokerite concentrations. Instead, the experimental data were found to fit satisfactorily to the Meter fluid model (Kozicki and Tiu, 1973; Meter and Bird, 1964) described by
and the new mixed Meter-and-Power Law fluid model given by:
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, p 0 are asymptotic Newtonian viscosities at low and high shear stresses, and zc is a characteristic shear stress value. where p,
The parameter values of both models were estimated through non-linear least square fitting (Bard, 1974) to the rheological data (Figure 15.I), and the results are given in Table 1. The mixed model reproduces the NAPL rheology over the entire region of measured shear stresses (Figure 15.1), but at sufficiently high shear stresses, Eq.(2), it predicts unrealistic viscosities lower than that of the paraffin oil, *,. For this reason, it was assumed that Eq.(2) is valid for T 5 7,, where p ( ~ , ) = p ~ , w h i lp e = p p for z > r s . An artificial 2D single fracture (dimensions: 150 mm x 110 mm) of controlled morphology (Figure 15.2a) was fabricated by etching mirror image patterns of a chamber-and-throat network on two glass plates with hydrofluoric acid, and sintering the glass plates in a programmable furnace (Avraam and Payatakes, 1995). The cross-sections of pore chambers and throats were eye-shaped (Figures 2b,c) and for flow studies, they can be approximated as ellipses. The pore dimensions of the artificial fracture are comparable to the corresponding measured ones of real fractures (Klint and Tsakiroglou, 2000). The chamber diameter ( (Wc) = 6OOpm ,(oc) = 150pm ) and throat width ( (WT) = 160pm, (oT) = 40 pm ) distributions were determined by estimating the relative variation of the pore sizes due to etching and sintering processes (Tsakiroglou and Payatakes, 1998). The pore depth distribution ((Wp) = 140pm ,(op) = 1Opm ) was estimated with reference to earlier measured ones and by matching the calculated absolute permeability of a theoretical pore network to the measured one of the glass micro-model, as explained below. Non-Newtonian NAPL flow experiments were performed on an artificial fracture (Figure 15.2a), at constant temperature, by adjusting the flow rate of injected NAPL, Q, with a syringe pump and measuring the pressure drop, AP, along a length, L, of the fracture with pressure transducers. The absolute permeability, k, of the fracture was determined by injecting Newtonian NAPL (paraffin oil), and fitted to the (linear) Darcy's relationship:
where A is the (apparent) cross-sectional area of the fracture, defined by:
A = WPl (4) where W is the total width of the fracture and P, is the length of periodicity, thus the constant distance between the centres of two adjacent chambers (Figure 15.2a).
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15.3 Theoretical flow of a fluid 15.3.1 Flow through a pore
The calculation of average flow velocity of a non-Newtonian fluid through a pore of arbitrary shape requires the simultaneous solution of the Navier-Stokes and rheological constitutive equations (Bird et al., 1977). Except for the most simple pore geometries and axisymmetric flows, this approach commonly results in a complex mathematical model that can be solved only numerically. Since the goal has been the modelling and simulation of non-linear flows in interconnected networks of numerous pores, a large number of runs and iterations would be required before obtaining results with satisfactory accuracy. Alternatively, the average velocity, U, of the flow of an inelastic, incompressible time-independent non-Newtonian liquid through a pore of arbitrary crosssection can approximately be described by the modified generalised Rabinowitsch-Mooney equation (Kozicki et al., 1966):
where r, is an hydraulic pore radius defined as the ratio of the area to the perimeter of the cross-section, a and b are geometric parameters depending exclusively on the shape of the flow cross-section (Kozicki et al., 1966), and T O is the average shear stress at the wall, given by:
The foregoing equations allow us to develop analytical (though approximate) solutions about the flow of non-Newtonian fluids through pores of complex geometry. By assuming that the fluid rheology is described by a Meter model, introducing Eq. (1) into Eq. ( S ) , and after some manipulation, the volumetric flow rate, Q,, is obtained through a pore, i, as a function of the constant pressure difference, APi , imposed along its length, Li:
ti,
hkli(k=O,l,2,3,...) are parameters of pore geometry and NAPL where, rheology given by
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= p,/~~ , whereas g, and g,, are hydraulic pore conductance
given by
respectively, where Ai is the pore cross-section area. Such an analysis can be repeated for the flow of a Mixed Meter and Power Law fluid Eq.(2) through a pore, to obtain:
where K1/m
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15.3.2 Flow through a single fracture A theoretical two-dimensional square chamber-and-throat network was computeraided constructed by using as input parameters the structural characteristics of an artificial fracture. The values of parameters A,, a,, bi, rHlwere calculated for each pore-throat by assuming elliptical cross-sectional shape. For the determination of the network permeability, a fixed pressure difference was imposed along one direction, and the flow of a Newtonian fluid was solved by formulating mass balances at each node of the network (Shah and Yortsos, 1995; Sahimi, 1996) (namely, by setting the algebraic sum of the flow rates of bonds adjoining to each node equal to zero), calculating the hydraulic conductance of the bonds for linear flow (namely, by omitting the second non-linear term of the right-hand of Eq. (7)), solving the resulting system of coupled linear equations with an iterative GaussSeidel method, and using Darcy's law, Eq.(3). The hydraulic resistance of each bond was calculated from the sum of the resistance of the throat and the resistance of the two connected chambers which were considered as equivalent capillary tubes with width and length equal to the half of their diameter. A mean effective pore throat length, P,,, (PLeff= ?tunneling-depth 16)) ........
->
(assert (voxel ?grid-object "settlement likely")) (update-information-base ?grid-object)(capi:confirm-yes-or-no "Do you want to visualize the results now"))
Such algorithms are used for decision tree generation and knowledge extraction from the available data (Figure 22.6). These include inductive learning, backpropagation, Bayes classification, and case-based reasoning. Details are provided by Quinlan (1993), Taylor et al. (1994), and Kohavi et al. (1996). The algorithms spot patterns or relationships in the data, and then extract knowledge in terms of rules and decision trees (Table 22.1).
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22.4 Conclusions The two case studies have demonstrated some of the capabilities of 3D GIs as a modelling and problem-solving tool for civil engineering projects. The current state of 3D GIS technology nevertheless has shortcomings. In terms of volume representation and property analysis, the detail required in the shallow subsurface characterisation for construction and the efficiency in handling such detail have not yet reached a desirable level. Moderately complex mathematical calculations and the incorporation of accepted rocWsoil mechanics models are not yet supported. The ease of exchange of model structures between 3D GIs and FEM applications is limited to simple situations, but can nevertheless facilitate the effective use of subsurface information in the design and construction stages. An integrated system of tools and techniques from 3D GIs, improved visualisation and artificial intelligence components will bring the necessary power and functionality to support effective decision making in large ground engineering projects. Table 22.1. Categorical attributes for training the knowledge-based system
I1
a) a simplij?ed table of categorical attributes (structure) ATTRIBUTE I POSSIBLE VALUES sand, clay, peat Geology unknown, low, medium, high Permeability unknown, range A, range B, range C, range D CPT b) a simplified table oftraining set [When the structure (a) and the training set are provided, the complete test set (unclassz~eddata) including millions of instances can be induced] CLASS PERMEABILITY CPT GEOLOGY class 1 Unknown unknown Sand class 6 Medium range A Peat class 4 range C High Peat unknown class 2 Unknown Clay class 3 range B High Peat class 5 range D High Peat
1
c) a decision tree modelfor the training set in (b)
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Figure 22.6. Stages of machine learning and the rule induction process
References Attewell PG, Toll DG (1990) Use of an Expert System in Site Investigation, Proceedings of 6IhInternational Congress of lnternational Association of Engineering Geology, D.G. Price (Eds), 1, 767-773, Balkema, Rotterdam CUR (1996) Building on Soft Soils, Centre for Civil Engineering Research and Codes, Balkema, Rotterdam, the Netherlands, 389 pp. Houlding SW (1994) 3D Geoscience Modelling, Computer Techniques for Geological Characterization, Springer-Verlag, Berlin Kohavi R, Sommerfield D, Dougherty J (1996) Data Mining Using MLC++, a Machine Learning Library in C++, Proceedings of the Eight lnternational Conference on Tools With Artificial Intelligence ICTA1'96, IEEE Computer Society Press Lispworks-Knowledgeworks(1997) User Guide, Harlequin Limited, Cambridge, UK Lynx (1997) Lynx Geoscience Modelling System, User Guide, Lynx Geosystems Inc., Vancouver, Canada Myers JC (1997) Geostatistical Error Managements: quantifying uncertainty for environmental sampling and mapping, van Nostrand Reinhold, New York Orlic B, Hack HRGK (1994) Three-Dimensional Modelling in Engineering Geology,. Proceedings of the 7th Congress of the International Association of Engineering Geology, 6,4693-4699 Orlic B (1997) Predicting Subsurface Conditions for Geotechnical Modelling, PhD thesis, ITC publication number 55, ITC-Delft, The Netherlands Quinlan JR (1993) C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, Inc., Los Altos, California Taylor C, Michie D, Spiegalther D (1994) Machine Learning, Neural and Statistical Classification, Paramount Publishing International Turner AK (1992) Three-Dimensional Modelling with Geoscientific Information Systems, Proc. of the NATO Adv. Res. Workshops,S.Barbara, California
23 Rock mass quality assessment based on deformation: experience from recent tunnels Villy Kontogianni, Stathis Stiros Patras University, Greece
23.1 Introduction The excavation of a tunnel re-arranges the stress field around the void, which tends to close due to the weight of the overlying rock mass. Tunnel closure is proportional to the void dimensions. For large tunnels (with a radius larger than 10 m and a length longer than 250 m), this takes the form of either a reduction of the tunnel section or a partial or total collapse (Figure 23.1), and thus represents a threat for excavation. An attempt is made in this paper to establish the suitability and adequacy of a Geological Strength Index (GSI) in characterising various rock types, including those with low strength and high deformability (Hoek, 1994; Hoek and Brown, 1998). Tunnel closure is an especially important consideration in the case of lowstrength rocks having a GSI less than 25, and where there is a thick (hundreds of metres) overburden (Kovari et al., 1993). Strain values in excess of 2% require a special consideration in terms of support. Higher values may significantly reduce the tunnel diameter (Sakurai, 1983), making necessary its re-excavation; a process producing delays and a dramatic increase in cost of the work.
23.2 Design approach The first approach to designing and economic and safe underground opening is to establish the geological conditions. Several rock mass quality rating systems have been introduced for such characterisation. The most popular are the Rock Mass Rating (RMR) system introduced by Bieniawski (1973, 1979) and the NGI (Q) system (Barton et al., 1974, 1988, 1994). Both the RMR and Q systems use the parameter Rock Quality Designation (RQD) and the compressive strength of the intact rock material (.,) for estimation of the rock mass quality; both parameters are deduced from ground investigation. Poor quality rock yields relatively few intact core pieces longer than 100 rnrn and hence it is difficult to determine either the RQD or the ., parameter. This M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 261–268, 2003. c Springer-Verlag Berlin Heidelberg 2003
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makes it difficult to calculate a reliable RMR or Q value (Hoek and Brown, 1998). In addition, both methods have been formulated from tunneling experience on fair to good quality rock masses (Xidakis, 1994; Marinos, 1994) so limiting their applicability to lower quality ground. More recently (Hoek, 1994; Hoek et al., 1995) the Geological Strength Index system has been introduced to characterise the rock mass. This is based on the visual impression of the rock structure (blockiness and surface condition). GSI can be applied to lower quality rock masses since it does not necessitate sampling. However, several rock mass types are difficult to describe by the GSI approach: weak rocks of low strength and high deformability, and sheared, folded, faulted, and mylonitised rocks (Hoek et al., 1998). The key question concerns the success with which GSI can be applied. This question is now considered using data from five recent tunnels in Greece, excavated in rock masses where GSI ranges from 15 to 65 at depths in the range 45 to 120 m. Data from the fifth tunnel (Tymfristos) excavated in very low quality ground could not be classified according to the existing rating system. Extensive convergence occurred, and an 'equivalent GSI value', GSI,, has thus been introduced. This parameter is deduced from an inverse analysis of the tunnel deformation during or shortly after the excavation. The analysis uses the 'characteristic line' method (Hoek,1999) for predicting tunnel deformation.
Figure 23.1. Tunnel deformation: (a) reduction of the tunnel section, (b) partial collapse, (c) total collapse. Shading indicates mass intruding the excavated area. After Kovari and Amstad (1993)
23.3 Prediction of tunnel convergence Having characterised the rock mass, the next step is to design the support system and predict the expected deformation. Prediction of tunnel convergence is usually based on two different approaches. The first approach is based on the theory of a 'characteristic line' (Hoek, 1999), an approximate method computing tunnel convergence and the thickness of the plastic zone produced during excavation. This theory is based on the following assumptions: (i) the tunnel section is circular and (ii) the stress field along the tunnel periphery is constant.
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In the second approach, prediction of the tunnel closure is made on the basis of a numerical analysis. Current practice utilises a 2D plastic Finite Element Modelling (FEM) approach and the PHASES software (Rocscience, 2000). This software permits a 2D study of non-linear deformation of rock, using both the MohrCoulomb criterion for low quality material and the Hoek-Brown failure criterion for better quality rock (Hoek and Brown, 1998). In the present analysis the 'characteristic line' method does not take into account the support system capacity. Therefore, the predicted deformation using the 'characteristic line' is expected to be larger than the convergence predicted using FEM or the actual deformation during tunnel excavation, since a temporary support system was usually installed.
23.4 Monitoring tunnel convergence Tunnel deformation has in the past been usually measured with INVAR tapes and wires (Kovari et al., 1993). These can only measure changes in the distance between selected points fixed on the tunnel periphery and provide an accuracy of 0.13 mm over 10 m (Dunnicliff, 1993). More recently, geodetic techniques have permitted the monitoring of both the reduction of the tunnel radius and the subsidence of its roof. These techniques are simple, inexpensive, functional and do not cause delays to the advancing excavation works. Measurements are based on the principles of tacheometry utilising 'total station' geodetic instruments. Measurements are taken periodically (usually once per day) until stabilisation of the height of all targets is obtained, usually over a two-month period (Kontogianni et al., 1999). A comparison between predicted and observed radius reduction was made in five tunnels. These employ the GSI parameter and the values of vertical strain. All tunnels studied have approximately the same diameter (10-12 m). The main parameters differing between each case study are the quality of the surrounding rock mass and the type of temporary support employed. The expected convergence for each tunnel was predicted using the 'characteristic line' calculation (Hoek, 1999) and FEM. Figure 23.2 summarises predicted and observed initial strain relative to the GSI of the rock masses for the five tunnels. Strain is computed as the ratio of ceiling subsidence to the tunnel diameter, so that data from tunnel sections with a different diameter are comparable. The main outcome is that the convergence values seem to follow exponential curves for the two tunnelling depths. Refer to Kontogianni et al. (2002) for a detailed analysis of the deformation, taking into account the support systems.
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Figure 23.2. Predicted and observed tunnel convergence plotted against GSI: (a) predicted tunnel convergence based on the characteristic line method (Hoek, 1999), (b) iredicted tunnel convergence (%) based on the Finite Element Method, (c) observed tunnel convergence using geodetic techmques. Diamonds indicate the available data for 120 m and squares for 50 m depth. Solid lines are the best fitting exponential lines. After Kontogianni et al. (2002)
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23.5 Tymfristos tunnel and the equivalent GSI, Based on the initial geological and geotechnical studies for the Tymfristos tunnel, the measured RMR parameter of the order of 15-20 indicated a poor to fair quality rock. This would suggest subsidence in the order of 10-70 mm based on the 'characteristic line' theory for an unsupported section. The observed vertical subsidence was much larger: 200-910 mm (Table 23.1). It is therefore evident that the estimated RMR value roughly corresponding to GSI=30 significantly overestimates the rock mass quality. One possible explanation is that the geotechnical study ignored the rock mass being highly tectonised, and consequently its strength has been significantly reduced. GSI was designed as parameter for describing better quality rock masses with a minimum GSI value of 25. To overcome this limitation, a new parameter, the equivalent GSI, has been introduced. This is calculated from an inverse analysis of the available data together with the 'characteristic line' formula. Table 23.1. Observed and calculated convergence for 10 sections beneath 37 to 94 m overburden in the Tymfristos Tunnel. The computed deformations are based on the initial GSI values, and are substantially lower than those observed. The sections having thick overburden (120-140 m) experienced even larger convergences
Section
Overburden height (m)
RMR
GSI
1
37
16
30
Calculated convergence (mm) 14
Observed convergence (mm> 328
More explicitly, the observed deformation after completion of the tunnel is used as well as the parameter (the intact rock mass strength). The result is an estimate of the value of GSI which correlates with the observed convergence. The 'characteristic line' analysis, using the two input parameters GSI and *,i,is able to predict the convergence. If one of these input parameters lies between two end-point values, the output estimate will accordingly fall between a minimum and a maximum convergence. In the inverse analysis, for any value of the *ci a
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curve f., reflecting the relationship between convergence and GSI, is drawn. The common point of this curve and the straight line corresponding to the observed convergence defines the GSI, value. However, instead of a precise value, a range of *, values, between a minimum and a maximum is usually available. For this reason, two f, curves can be constructed and GSI, will then correspond to any point of a line segment. More explicitly, this segment is defined as the part of the line corresponding to the observed convergence bounded by the two f curves (Figure 23.3). The observed convergence is just an upper bound value since this is calculated on the basis of the 'characteristic line' theory, referring to supported sections. Larger deformations would be expected where there is no support. With no support, instead of a line segment bounded by the two f, curves, it is reasonable to consider an areal distribution for GSIa bounded by the two curves, the line of the observed convergence and a line corresponding to an unknown convergence of the unsupported sections (Figure 23.3).
€
O$&,
"*-0ci=5MPa
Unknown deformation for section ----A'-" /unsupported deformation
20 30 GSI I GSle Figure 23.3. Example to estimate GSIc.Convergence is plotted on a logarithmic scale. The GSY GSIaratio is deduced from the 'characteristic line' analysis, and values are limited to the shaded zone (after Tzortzis, 2001)
If it is further assumed that the rock mass strength and in situ stress along a specific segment are nearly constant, data from n tunnel sections would lead to n diagrams similar to Figure 23.3, each with slightly different areas of GSI, distribution. These areas would normally overlap and their 'centre of gravity' would correspond to the best statistical estimate of GSI, for the examined segment (i.e. a 'mean' GSI,). However, given the uncertainties of the data, it is wiser to think of a zone for GSI, instead of a single value. This process was applied to a section of the Tymfristos tunnel having similar overburden thickness, oCi,and lithology. A mean GSI, of the order of 4-15 corre-
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sponding to the shaded zone of Figure 23.4 was computed; these values of GSI, are extremely low and cannot be classified by the GSI system. 1m,o
50m overburden
\
30 40 50 60 GSI I GSle Figure 23.4. Mean GSIe and observed convergence values for the Tymfristos tunnel (grey area), comparing GSI with convergence (calculated using the 'characteristic line' method) for the five tunnels (using data for Figure 23.2a). The depth of the Tymfristos tunnel lies in the range 37 to 94 m (modified from Tzortzis, 2001) 0
10
20
23.6 Conclusions The classification of the very weak rock mass characterising the Tymfristos tunnel was achieved by developing the GSI parameter, introducing the equivalent GSI parameter, GSIe. Based on an inverse analysis using the 'characteristic line' method, deformations and GSI values for different tunnels have been compared. This has yielded a very useful tool for predicting the tunnel behaviour prior to excavation.
Acknowledgments The authors are indebted to Dr. C. Tsatsanifos (PANGAEA S.A.) for providing unpublished geotechnical and geodetic data for the Tymfrktos tunnel discussed in this paper.
References Dunnicliff J (1993) Geotechnical Instrumentation for Monitoring Field Performance. John Wiley & Sons, Inc., U.S.A., 577 pp.
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Hoek E (1994) Strength of rock and rock masses. ISRM News Journal, 2(2), 4-16 Hoek E (1999) Support for very weak rock associated with faults and shear zones. Int. Symp. on Rock Support and Reinforcement Practice in Mining. Kalgoorlie, Australia Hoek E, Brown ET (1998) Practical estimates of rock mass strength. Int. J. Rock Mech. Min. Sci. 34 1165-1 186 Hoek E, Marinos P, Benissi M (1998) The applicability of the geological strength index (GSI) for weak and sheared rock masses. The case of the Athens "schst" system of formations. Bull. of the Int. Assoc. of Eng. Geology, 57, 151-160 Kontogianni V, Tesseris D, Stiros S (1999) Efficiency of geodetic data to control tunnel deformation. Proc. of The 9" FIG International Symposium on Deformation Measurements. Olsztyn, Poland. 206-214 Kontogianni V, Stiros S (2002) Shallow tunnel convergence: predictions and observations. Engineering Geology, 63,333-345 Kovari K, Amstad C (1993) Decision making in tunnelling based on field measurements. Compressive rock engineering, Bergarnon 4, 57 1-606 Rocscience Inc. (2000) Toronto (www.rocscience.com) Sakurai S (1983) Displacement measurements associated with the design of underground openings. Proc. Int. Symp. Field measurements in Geomechanics, Zurich 2, 11631178 Tsatsanifos CP, Mantziaras PM, Georgiou,D (1999) Squeezing rock response to NATM Tunneling. A case study. Proc. of the Int. Symp. on Geotechnical Aspects of Underground Construction in Soft Rock. Japan, 167-172 Tzortzis V (2001) Tymfristos tunnel convergence (in Greek). BSc degree thesis. Geodesy Laboratory, Patras University. [unpublished]
24 Improving subsurface parameter estimation through guiding geophysical inversions with a priori geological data Peter Jackson British Geological Survey, Keyworth, UK
24.1 Introduction Two-dimensional resistivity inversion is a very popular non-invasive method for imaging the subsurface. The example shown in Figure 24.1 was downloaded from the World Wide Web and illustrates the range of information that can now be obtained. The extensive use of supporting boreholes and the 'smoothness' of the resistivity model are readily apparent. Resistivity inversion, in common with other geophysical methods, requires a constraint to stabilise the process of deriving a resistivity model from a measurement dataset. The role of the constraint is to ensure 'smoothness' or stability of the solution, or to reflect a priori judgements about the likelihood of a solution (e.g. Press et al., 1992). The method of choice for constraining these inversions limits the spatial roughness of the resistivity model, resulting in 'smooth' solutions. This method is often referred to in the geophysical literature as 'smoothness' constrained inversion, being inspired by Occam's razor concerning the merits of the simplest solution (e.g. Constable et al., 1987). However, even at the site scale, the subsurface geology very often varies abruptly with location. For example: dissolution cavities, faults, lithological boundaries and glacial till. Consequently, a smooth resistivity model is often incompatible with subsurface conditions. The elements of a typical resistivity inversion are shown in Figure 24.2 along with the corresponding matrix equation depicted in Figure 24.3 (e.g. Constable et al., 1987; Sasaki, 1994; Loke and Barker, 1996). This mathematical procedure minimises both the 'data mismatch' and the 'penalty' with the scalar h controlling the weighting between them. High h values result in low 'penalties' (smooth resistivity models) and high 'data mismatches'. Conversely, low h values result in high 'penalties' (rough resistivity models) and low 'data mismatches' (good agreement between 'field' and 'calculated' measurement datasets). Press et al. (1992) describe the role of the constraint in terms of 'regularising or stabilising a highly degenerate optimisation process that would otherwise oscillate between local minima'. Figure 24.4 illustrates their solution space, suggesting the region of 'best' solutions lies in between these two extremes. M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 269–282, 2003. c Springer-Verlag Berlin Heidelberg 2003
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Figure 24.1. Bedrock mapping in karst terrain: an example of subsurface resistivity imaging from the World Wide Web: http://www.agiusa.com~brochure~schnabel~k~st.shtml
rtmdv rkal W'Ih'C
-
-
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Figure 24.2. Schematic diagram showing the elements of resistivity inversion
9iwluc
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Mathematics of Resistivity Inversion
'Jacobian'
/
regularising matrix penalty = hTh (h=R.x)
unknown resistivities
r is the set of 'field' measurements (vector) s is the standard deviation of the 'field' measurements m is the set of measurements simulated using x (vector) A Lagrange multiplier controlling the weighting between 'data-misfit' and 'penalty' Figure 24.3. A 'smoothness' constrained non-linear least-squares inversion scheme (after Jackson et al., 2001)
Figure 24.4. The constraint ensures 'smoothness' or stability of the solution, or reflects a priori judgements about the likelihood of a solution (adapted from Press et al., 1992)
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24.2 Testing Resistivity Inversion Recently published results of numerical testing of resistivity inversion are illustrated in Figure 24.5. Here, 'field' measurement datasets were calculated for a series of 2D, common, geological ground models: fault without overburden, buried graben and corresponding horst structures (Olayinka and Yaramanci, 2000). Olayinka and Yaramanci inverted these synthetic 'field' data sets using a well known, commercially available, software package RES2DINV (Loke and Barker, 1996). By inspection, the inverted resistivity model can be seen to be far smoother than the resistivity model used to calculate the 'field' measurements, the resistivity values themselves being correspondingly different, of the order of 100% in many cases. A similar, 3D example of resistivity inversion is illustrated in Figure 24.6. Here a surface array of 200 potential electrodes, at 1 m spacing, can be seen associated with a sub-set of 12 electrodes used to pass current, enabling both components (Ex and Ey) of the resulting electric field to be measured, resulting in over 4000 measurements. The measurement dataset was calculated using the 'true' resistivity model depicted in Figure 24.6 with noise, typical of a greenfield site in the UK, being added to it (Jackson et al., 2001). The results of inversions based on the equation in Figure 24.3 can be seen in Figure 24.7 where the inverted resistivity models using both a coarse and a fine reconstruction mesh are compared. The 'true' resistivity model, shown in Figure 24.6, is compatible with both the coarse and fine meshes. For the coarse mesh, the corresponding inverted resistivity model, having 112 voxels (7 x 4 x 4), is almost identical to the 'true' model. On the other hand, the inverted resistivity model associated with the fine mesh, having 1170 voxels (13 x 9 x10) can be seen to be significantly different to the 'true' model. Spatial smearing is evident at the base of the resistive structure, and significant 'overshoot' can be seen at higher levels within the structure. For example, resistivity values at the core of the structure at a depth of Z= -4 m can be seen to be 50% too high, and those at a depth of Z= -8 m are in error by 200%. These results, being 'too smooth', are similar to those described by Olayinka and Yaramanci (2000) described above. As illustrated in Figure 24.8, when considering 'smoothness' constrained inversion, an abrupt spatial change in the resistivity model results in a high 'penalty' while a smooth change results in a low one. Consequently, a high 'penalty' due to a 'step' change in resistivity will be reduced during inversion, in turn leading to a correspondingly smooth inverted resistivity model.
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Figure 24.5.2D numerical experiments (adapted from Olayinka and Yaramanci, 2000)
Figure 24.6. Electrode array (X-Y plane) and the 'true' resistivity model used in numerical experiments to test two resistivity inversions (based on Jackson et al., 2001)
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Figure 24.7. Resistivity models from inversions using 112 (upper) and 1170 (lower) measurements calculated from the resistivity model shown in Figure 24.6 (based on Jackson et al., 2001)
Figure 24.8. 'Smoothness' constraints: penalties arising from smooth and abrupt spatial changes in the inverted resistivity model (based on Sasaki, 1994)
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24.3 Constructing a constraint compatible with 'step' changes in resistivity Considering likely subsurface conditions is a prerequisite to developing compatible constraints, the central idea being that the 'penalty' should be low for situations that are to be expected on the basis of the ground model developed from existing geological and geotechnical information. As illustrated in Figure 24.9, the resistivity of earth materials is highly variable and, when viewed globally, many lithologies can exhibit the same resistivity. These resistivity values are controlled by: porosity, pore-water saturation, pore-water resistivity, porosity style (e.g. cementation and particle shape) and matrix conduction (Archie, 1942; Brace and Orange, 1968; Grant and West, 1965; Keller and Frischknecht, 1966; Mualem and Friedman, 1991; Parkhomenko, 1967; Jackson et al., 1978). However, this is not normally the case within any one formation or sequence. A typical situation is shown in Figure 24.10 where the resistivity values can be seen to be diagnostic of sandstone and shale respectively. Furthermore, resistivity is used quantitatively as the primary property in the prediction of the fraction of pore space filled with water or oil (Archie, 1942). A typical ground model might consist of various lithologies each exhibiting different ranges of resistivity values, modified by geological processes such as faulting and/or anthropogenic ones such as mining. A generic ground model typical of coalfields in western Europe exhibiting these attributes can be seen in Figure 24.11. The resistivity of coal is controlled by its ash content, high purity coal being characterised by values less than 10 ohm-m (Carmichael, 1982). Thus, coal provides a third lithology exhibiting a characteristic range of resistivity values, in addition to sandstone and shale. The resistivity of mine workings, such as those depicted in Figure 24.1 1, would be determined by their contents, normally either air or water, the resistivity of both being constant and readily assessed with a high degree of relative accuracy. RediviIy I ' o h m ) 1 l€F td 1 6 Eb
Figure 24.9. Ranges of resistivity values for shale and sandstone in Europe (based on Kearey and Brooks, 1984)
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Figure 24.10. Typical field results in a sandstone-shale sequence, showing the ranges of resistivities changing abruptly between lithologies (based on Keller and Frischknecht, 1966)
Figure 24.11. A geological model, typical of coalfields, showing sub-vertical faulting and sub-horizontal litho-stratigraphicboundaries that are to be expected (based on Fookes, 1997)
uo!le:,o~jo luapuadapul "X Inoqt! a3wl pauwalap-aid r! uwrm sanpa di!ngs!sai %U~AEYppom 41ags1sa.1patraau! ayl jo sauoz aw 3uFsn lnq ' 8 . ~ a2 ~ n Q %UIMO~~O pJa ~ a p p : ,s! ,x JOJ & p a d ayl :mp~suo:,,anpa-dq-padnoi~,-ZT-PZ am%d
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'grouping-by-value' is far closer to the 'true' resistivity model, the errors being seen in Figure 24.13 (lower) to be 10-15%. In summary, inverting using a constraint compatible with the ground-model improved the inverted resistivity model. Numerical experiment I
1
-- . . . -
1
Figure 24.13. Display of the inverted resistivity model at a depth of z = -4.0 m, with testing constraints starting from the true resistivity model (after Jackson et al., 2001)
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A field case study Resistivity models obtained by inverting measurements made over a faulted sandstone-shale sequence in the Saar coalfield are now described. On the basis of extensive trenching, the ground model (Figure 24.14) can be seen to be similar to the generic model in Figure 24.11 (Clasen, 1995). The location of a fault (Hanwiese) was identified at a site using ground probing radar (Greenwood et al., 1994), where resistive arkosic sandstone was juxtaposed with conductive shale. The electrode array used in the field is the same as the one shown in Figure 24.6, comprising 12 current sources and 200 potential electrodes, resulting in a dataset of 4177 measurements and is described in greater detail elsewhere (Jackson et al., 2001). The original inverted resistivity model obtained by applying the 'smoothness' constraint to the inversion can be seen on the left-hand side of Figure 24.15. Considering the depth range 2.5 to 3.5 m in Figure 24.15, a smooth transition in resistivity values can be seen across the fault; the corresponding ranges of resistivity values attributed to the sandstone and shale lithologies being 400 to 930 and 29 to 90 ohm-m respectively. In contrast, the corresponding resistivity model obtained using the 'grouped-byvalue' constraint includes an abrupt lateral change in resistivity across the fault in agreement with the distinct sandstone shale interface identified during trenching. The corresponding ranges of resistivity values for the sandstone and shale were far narrower, being 530 to 570 and 50 to 60 ohm-m respectively.
24.5 Conclusions 'Smoothness' constraints are the method of choice for inverting electrical resistivity measurements, but are often used without regard to whether the smoothness they impose on resistivity models can be justified in terms of subsurface conditions. Following a desk study to develop the ground model, a wealth of a priori information is normally available from which the range of possible subsurface resistivity distributions and their 'styles' may be deduced. Abrupt spatial changes in resistivity values are a 'style' of resistivity distribution commonly expected prior to surveying, for example. For this style of resistivity distribution, a new 'grouped-by-value' constraint has been developed which produced a low 'penalty'. This in turn has resulted in significant improvements to the estimation of resistivity values and a reduction in spatial 'smearing' as illustrated by both numerical and field examples. Consequently, it is proposed that such constraints be used on the basis they produce low 'penalties' when applied to resistivity models having 'styles' compatible with the a priori ground model.
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Acknowledgements This paper is published with the permission of the Executive Director, British Geological Survey (NERC).
References Archie GE (1942) The electrical resistivity log as an aid in determining some reservoir characteristics. Petroleum Technology, 5,l-8; Trans. AIME, 146,54-62 Beamish D, Clasen D, Greenwood PG, Peart R (1997) Novel regularised inversion of VLF(R) data and coincident radar sections over a probable fault affecting Carboniferous sedimentary rocks in the Saar region, Germany, In: McCann DM, Eddleston My Fenning PJ, Reeves GM (eds) Modern Geophysics in Engineering Geology. London, The Geological Society, Special Publication No. 12,45-52 Brace WF, Orange AS (1968) Further studies of the effect of pressure on electrical resistivity of water saturated crystalline rocks. Journal of Geophysical Research, 73,5407 Carmichael RS (1982) Handbook of physical properties of rocks. Boca Raton, CRC Press Inc., 404 pp. Clasen D (1995) Exploration of tectonic faults, EC contract no. 7220-AF1136, Saarbrucken, Saarberwerke AG Constable SC, Parker RL, Constable CG (1987) Occam's inversion - a practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics, 52, 289-300 Fookes PG (1997) Geology for Engineers: the Geological Model, Prediction and Perforrnance, The First Glossop Lecture. Quarterly Journal of Engineering Geology, 30 (4), 293-43 1 Grant FS, West GF (1965) Interpretation Theory in Applied Geophysics, London, McGraw-Hill Book Company, 583 pp. Greenwood PG, Jackson PD, Meldrum PI, Peart RJ, Raines MG (1994) Methods for recognition of geological weakness and other surface discontinuities caused by underground mining in Carboniferous terrain, British Geological Survey, Technical Report WNl94102, 37 pp. Jackson PD, Taylor-Smith D, Stanford PN (1978) Resistivity-porosity-particleshape relationships for marine sands. Geophysics, 43, 1250-1 268 Jackson PD, Earl SJ, Reece GJ (2001) 3D resistivity inversion using 2D measurements of the electric field, Geophysical Prospecting, 49 (I), 1-15 Kearey P, Brooks M (1984) An introduction to geophysical exploration, Oxford, Blackwell Scientific Publications, 296 pp. Keller GV, Frischknecht FC (1966) Electrical methods in geophysical prospecting, Oxford, Pergamon Press, 5 19 pp. Loke MH, Barker RD (1996) Rapid least-squares inversion of apparent resistivity pseudosections by a quasi-Newton method. Geophysical Prospecting, 44, 131-1 52 Mualem Y, Friedman SP (1991) Theoretical prediction of electrical conductivity in saturated and unsaturated soil. Water Resources Research, 27(10), 2771-2777 Olayinka AI, Yaramanci U (2000) Assessment of the reliability of 2D inversion of apparent resistivity data. Geophysical Prospecting, 48(2), 293-3 16
Chapter 24
Improving subsurface parameter estimation
28 1
Parkhomenko EI (1967) Electrical Properties of Rocks, New York, Plenum Press, 3 14 pp. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical Recipes in FORTRAN, 2nd edition. Cambridge University Press: 963 pp. Sasaki Y (1994) 3D resistivity inversion using the finite element method. Geophysics, 59(1 I), 453-463
Faulted sandstone-shale sequence Geological sections (Test Area 2.1 Lummerschied-Kutzhof) N NtN
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Figure 24.14. Testing two constraints by starting from the true resistivity model (after Clasen, 1995)
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Faulted sandstone-shale sequence -Smoothness' constraint
'Grouped-by-value' constraint
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Figure 24.15. Inverting using the 'grouped-by-value' constraint resulting in approximately constant values of resistivity for the sandstones and shales in the depth range 2.5 to 3.5 m, in contrast to the results obtained using the 'smoothness'constraint (after Jackson et al., 2001)
25 Characterisation of void space utilising pore network models Christos Tsakiroglou Institute of Chemical Engineering and High Temperature Chemical Processes, Foundation for Research and Technology, Hellas, Greece
25.1 Introduction The micro-fabric of porous rocks influences a variety of transport processes of geological significance, for instance the contamination of groundwater. Transport coefficients (e.g. capillary pressure curves, absolute and relative permeability, hydrodynamic dispersion coefficients, formation factor and resistivity index) bridge the gap between pore-scale events and fluid transport at the macro-scale of a groundwater reservoir. These incorporate the microscopic dynamics within the macroscopic mass, momentum and energy balances (Celia et al., 1989; Hassanizadeh and Gray, 1993). Such coefficients are complex functions (Blunt et al., 1992; Ioannidis and Chatzis, 1993; Lowry and Miller, 1995; Tsakiroglou and Fleury, 1999), dimensionless flow rate parameters coupled with physicochemical properties of fluids (Avraam and Payatakes, 1995; Constantinides and Payatakes, 1996), wettability (Blunt, 1998) and saturation history (Dullien, 1992; Sahimi, 1995).
25.2 Pores Optical microscopy in combination with image analysis techniques has been used for: (1) the determination of geometrical properties of 3D pore space by processing random sections with the aid of quantitative stereology (Underwood, 1970), and (2) the estimation of topological properties of the interconnected pore structure by analysing serial sections (DeHoff and Rhines, 1968; Lymberopoulos and Payatakes, 1992; Macdonald et al., 1986). Mercury porosimetry is a fast technique for determining the capillary pressure curves. A methodology for characterising the pore structure of relatively macroporous materials has recently been developed and applied successfully to an outcrop of Grey-Vosgues sandstone (Tsakiroglou and Payatakes, 1997). The method combines data obtained from the serial sectioning analysis of porecasts with the M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 283–292, 2003. c Springer-Verlag Berlin Heidelberg 2003
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analysis of experimental mercury porosimetry curves using a pore network simulator of mercury intrusionlretraction.
25.3 Fractures Fractures are abundant in the ground and play an important role for the migration of fluids (D' Astous et al., 1989; Fredericia, 1990; Kueper and McWhorter, 1991). One of the key parameters of a fracture is its aperture. This is defined as the distance between two opposite fracture surfaces. The bulk hydraulic conductivity of fractured media is related to the fracture aperture through a cubic law (Snow, 1969) that is based on a model of parallel fractures composed of vertical and smooth surfaces. Although this model is rarely representative of real fractures, it is commonly used in conjunction with hydraulic tests for calculating the 'hydraulic aperture' of fractured rocks and soils (Tsang, 1992; Witherspoon et al., 1980). More advanced methods using bulk flow data, breakthrough curves and entry pressures are also available for the calculation of fracture aperture (Hinsby et al., 1996; Sidle et al., 1998). The true or 'mechanical aperture' must be measured directly on the fractured rock. Direct field measurements of realistic fracture aperture in soft sediments have not yet been performed. At the lab- scale, CAT-scanning images have been used to give rough estimates of the fracture apertures in granites and sandstones (Keller, 1998). Soft rocks tend to deform during sampling or measuring procedure and hence, the fractures must be sampled very carefully and stabilised before any measurement takes place. A typical methodology is the impregnation of the void space (fractures, macro-pores and matrix porosity) of samples with a resin (Gale, 1987; Gentier et al., 1989).
25.4 Characterising the micro-porous matrix of a clay till Clay till is a fine-grained glacial sediment that is widespread in the Northern Hemisphere. In spite of the progress achieved on the macroscopic mapping of complex fracture systems in fine-grained sediments (Bosscher and Connel, 1988), and particularly in clay tills (Jakobsen and Klint, 1999; Klint and Gravesen, 1999), no effective systematic methodology has yet been developed for the characterisation of clay till single fractures, in terms of the geometrical and topological properties of their structure. There is an apparent lack of integrated methods capable of determining the microscopic parameters of fractured porous media. In the present work, a methodology has been developed to determine the geometrical, topological and hydraulic properties of micro-porous matrices and single fractures encountered in clay till. Intact samples were collected from three pits excavated on an abandoned tar factory (Ringe Site) situated in Funen island, Denmark (Sidle et al., 1998). The samples were impregnated with a polyester resin through a 'wet' procedure de-
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scribed in detail in (Klint and Tsakiroglou, 2000) in order to avoid any undesirable disturbances of the initial structure. A small piece of the porecast (5 x 5 x 2 mm) was sliced and embedded in Araldite. Each new polished surface of the pore-cast was placed under an optical microscope, its image (picture) was taken with a video-camera, and was recorded on a videotape for further analysis (Figure 25.1). For the determination of geometrical properties, the algorithm GETPO has been developed (Lymberopoulos and Payatakes, 1992) which reconstructs the 3D pore network from data of 2D pore features of serial sections (X,Y co-ordinates of twodimensional pore sectionals). Here, the algorithm was used for the determination of the genus, which is a measure of the pore space connectivity (DeHoff and Rhines, 1968; Macdonald et al., 1986; Lymberopoulos and Payatakes, 1992), areal porosity and equivalent spherical diameter distribution of the 3D pore chambers (CSD). The CSD was derived from the equivalent circular diameter distribution of the 2D pore features, by using the Schwartz-Saltykov method of quantitative stereology (DeHoff and Rhines, 1968; Underwood, 1970). Each size distribution was fitted to a bimodal distribution function composed of two lognormal component distributions 1 and 2 by using the Marquardt method (Bard, 1974). The two porecasts were cut from a weathered and a non-weathered area of clay till. The mercury intrusion curves of clay till samples were relatively broad towards both low and high pressures (Figure 25.2). This characteristic feature of capillary pressure curves is associated with: (1) a wide throat size distribution, (2) strong spatial pore size correlation, (3) high fractal roughness porosity and (4) low pore network connectivity (Tsakiroglou and Payatakes, 1998). The shape of mercury intrusion curve shows that the breakthrough pressure (Drake and Ritter, 1945) is quite low and some of the pore volume may become accessible and filled with mercury through boundary pores having sizes larger than the upper size limit of mercury porosimetry (Dm-150.m). Therefore, mercury porosimetry may underestimate the porosity of macroporous materials such as the investigated clay till samples. For this reason, the porosity was considered as equal to the average areal porosity that was determined by image analysis of serial sections. The throat-diameter distribution (TSD) (Figure 25.3a) and the ratio of fractal to total porosity were estimated iteratively by comparing the simulated with the experimental mercury intrusion curve (Figure 25.3b). After each simulation, the absolute permeability, k, of the pore network was calculated by using an electrical analogue of the pore network and solving a system of coupled linear equations (Tsakiroglou and Fleury, 1999). After eight iterations, the experimental mercury intrusion curve could be predicted satisfactorily.
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Figure 25.1.
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100000
Pressure (kPa) Figure 25.2. Experimental mercury porosimetry curve of clay till (sample ct-1s)
25.5 SEM image analysis Optical studies of SEM images revealed (Klint and Tsakiroglou, 2000) that the aperture of clay till fractures resemble an interconnected irregular network of channels (Figure 25.4) rather than the void space left between two smooth parallel plates. A representative model of the aperture of clay till fractures is a network of elliptical channels of equal length (Figure 25.5). SEM image analysis was used to characterise the fracture aperture in terms of the geometrical parameters of 2D networks of elliptical channels of equal length.
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Thick sections of resin impregnated clay till fractured samples were prepared and photographed in visible incident and UV light for identifying fractures and characteristic clasts on the sample surface. Features belonging to the same fracture were identified and digitised (Figure 25.6) and the following parameters were measured: (1) the major axis length, defined by the two border pixels that are the farthest apart; (2) the minor axis length, drawn by two border pixels defining the longest line perpendicular to the major axis; (3) the X,Y co-ordinates of the binary centre of mass of each feature (geometric centre), calculated by averaging the pixel co-ordinates over the entire feature. Once all measurements had been performed, the statistical distribution functions of major and minor axis length were determined. The distances between geometric centres of adjacent features were determined and averaged over the entire section to calculate the mean distance between fracture features which was considered as an approximate estimate of the mean channel length (Underwood, 1970). The dimensions of the elliptical channels of the 2D network model were considered equal to the measured lengths of major and minor axes of the 2D features respectively.
25.6 Conclusions The microscopic pore space properties of the ground can be quantified in terms of pore network models by combining digitised images of serial sections with algorithms of 3D reconstruction, and mercury porosimetry data with deterministic simulators of mercury intrusionlretraction in chamber-and-throat networks. High connectivity, bimodal throat (TSD) and chamber (CSD) diameter distributions, strong spatial chamber-to-throat size correlation and intermediate values of the surface roughness fractal dimension and porosity were found for the matrix porosity of clay till samples. The analysis of SEM images allowed quantification of the aperture of single fractures in terms of networks of elliptical channels, whereas reliable estimates of the hydraulic properties of fractures can be obtained with the use of effective medium approximation (EMA) . Broad distributions of the minor and major channel axis lengths, strong spatial correlation of major to minor axis length, and absolute permeability differing significantly from that predicted by the cubic law were found for the clay till fractures studied. Critical path analysis offers promise for deriving closed relationships, allowing estimation of the absolute permeability of fractures from electrical measurements and microscopic properties of the channel network.
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Simulation 2 - - - - - . Simulation 4 ......... Simulation 6 -- -.-.-.. Simulation 8
Pressure (kPa) Figure 25.3. (a) Estimates of the throat diameter distribution from several iterative simulations, (b) Comparison of the simulated mercury intrusion curves with the experimental curve in Figure 25.2
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Figure 25.4. Identification of the channel network skeleton (white) and rootholes (yellow) on a transverse section of clay till fracture
Figure 25.5. Aperture model for a fracture in clay till
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-
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Figure 25.6. Digitised SEM images of a fracture in clay till
Acknowledgements This work was supported by EC (DG XII) as part of the Environment and Climate programme, Contract No. ENV4-CT97-0457, and the Energy, Environment and Sustainable Development programme, Contract No. EVK1-CT1999-00013. Dr. Johnny Fredericia, Mr. Knud E. Klint and Ms. Annette E. Rosenbom of the Geological Survey of Denmark and Greenland (GEUS) are acknowledged for providing resin-impregnated clay till samples and SEM images of 2D cross-sections of fractures as well as for useful discussions.
References Avraam DG, Payatakes AC (1995) Flow regimes and relative permeabilities during steadystate two-phase flow in porous media. J. Fluid Mech., 293,207-236 Bard Y (1974) Nonlinear parameter estimation. Academic Press, New York Blunt M (1998) Physically-based network modelling of multiphase flow in intermediatewet porous media. J. Pet.Sci. Eng., 20, 117-125
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Blunt M, King MJ, Scher H (1992) Simulation and theory of two-phase flow in porous media. Phys. Rev. A, 46,7680-7699 Bosscher JP, Connel DE (1988) Measurement and analysis of jointing properties in finegrained soils. J. Geo-technical Engineering, 114(7), 826-843 Celia MA, Kindred JS, Herrera I (1989) Contaminant transport and biodegradation. 1. A numerical model for reactive transport in porous media. Water Resour. Res., 25, 11411148 Constantinides GN, Payatakes AC (1996) Network simulation of steady-state two-phase flow in consolidated porous media. AIChE J., 42,369-382 D'Astous AY, Ruland WW, Bruce JRG, Cherry JA, Gillharn RW (1989) Fracture Effetcs in the Shallow Groundwater Zone in Weathered Sarnia Area Clay. Can. Geotech. J. 26, 43-56 DeHoff RT, Rhines FN (1968) Quantitative microscopy, McGraw-Hill, New York Drake LC, Ritter HL (1945) Macropore size distributions in some typical porous substances. Ind. Chem. Eng. Anal. Ed., 17,787-791 Dullien FAL (1992) Porous media: Fluid transport and pore structure. Academic Press, San Diego, California Fredericia J (1990) Saturated hydraulic conductivity of clayey till and the role of fractures. Nordic Hydrology 21, 119-132 Gale JE (1987) Comparison of coupled fracture deformation and fluid flow models with direct measuremnts of fracture pore structure and stress-flow properties. In: Farmer IW et al. (eds.) Proceedings of the 28th US Symposium of Rock Mechanics, June 29-July 1, Tucson, Arizona, 1213-1222 Gentier S, Billaux D, Vliet LV (1989) Laboratory testing of the voids of a fracture. Rock Mechanics, 22 (B14), 149-157 Hassanizadeh SM, Gray WG (1993) Toward an improved description of the physics of twophase flow. Adv. Water Resour., 16,53-67 Hinsby K, McKay LD, Jorgensen PR, Lenczewski M, Gerba CP (1996) Fracture aperture measurements and migration of solutes, viruses and immiscible creosote in a column of clay-rich till. Ground Water 34(6), 1065-1075 Ioannidis MA, Chatzis I (1993) Network modelling of pore structure and transport properties of porous media. Chem. Eng. Sci., 48,95 1-972 Jakobsen PR, Klint KES (1999) Fracture distribution and migration of DNAPL in a clayey lodgment till. Nordic Hydrology, 30 (4/5), 285-300 Keller AA (1997) High resolution CAT imaging of fractures in consolidated materials. Int. J. Rock Mech. Min. Sci., 34 (3/4), 358-375 Klint KES, Gravesen P (1999) Fractures and biopores in Weichselian clayey till aquitards at Flakkebjerg, Denmark. Nordic Hydrology, 30 (4/5), 267-284 Klint KE, Tsakiroglou CD (2000) A new method of fracture aperture characterization. In: VA Tsihrintzis et al. (eds) Proc. Intern. Confer. Protection and Restoration of the Environment V, Thassos Island, Greece, 2-6 July 2000, 127-136 Kueper BH, McWhorter DB (1991) The behavior of dense, nonaqueous phase liquids in fractured clay and rock. Ground Water, 29(5), 716-728 Lowry MI, Miller CT (1995) Pore-scale modelling of nonwetting phase residual in porous media. Water Resour. Res., 31,455-473 Lymberopoulos DP, Payatakes AC (1992) Derivation of topological, geometrical and correlational properties of porous media from pore-chart analysis of serial tomography data. J. Colloid Interface Sci., 150, 61-80
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Macdonald IF, Kaufman P, Dullien FAL (1986) Quantitative image analysis of finite porous media. 11. Specific genus of cubic lattice models and berea sandstone. J. Microscopy, 144,297-3 16 Sahimi M (1995) Flow and transport in porous media and fractured rock. VCH, Weinheim, Germany Sidle RC, Nilsson B, Hansen M, Fredericia J (1998) Spatially varying hydraulic and solute characteristics of a fractured till determined by field tracer tests, Funen, Denmark. Water Resour. Res., 34,25 15-2527 Snow DT (1969) Anisotropic permeability of fractured media. Water Resources Research, 5,1273-1289 Tsakiroglou CD, Payatakes AC (1997) Analysis of the topological and geometrical characteristics of the pore space of permeable solids using serial tomography, mercury porosimetry and theoretical simulation. In: B McEnaney et al (eds) Proceedings of the IUPAC Symposium (COPS-IV), Bath, UK, 15-18 Sept. 1996,204-212, Royal Society of Chemistry, London Tsakiroglou CD, Payatakes AC (1998) Mercury intrusion and retraction in model porous media. Adv. Colloid Interface Sci., 75,215-253 Tsakiroglou CD, Fleury M (1999) Pore network analysis of resistivity index for water-wet porous media. Transport in Porous Media 35, 89-128 Tsang YW (1992) Usage of equivalent apertures for rock fractures as derived from hydraulic and tracer tests. Water Resources Research, 28(5), 1451-1455 Underwood EE (1970) Quantitative stereology. Addison-Wesley, New York Witherspoon PA, Wang JSY, Iwai K, Gale JE (1980) Validity of cubic law for fluid flow in a deformable rock. Water Resources Research, 16(6), 1016-1024
26 Assisting data interpretation: the cone penetration test Jos Maccabiani, Dominique Ngan-Tillard, Peter Verhoef Delft University of Technology, The Netherlands
26.1 Introduction The construction of a subsurface model requires considerable expert knowledge, tends to be labour intensive, and may have a significant degree of data redundancy, especially for very large projects. Every year in the Netherlands, about 30,000 Cone Penetration Tests (CPTs) are performed, yet just 5,000 boreholes are sunk. Consequently, many Dutch institutions and companies have compiled extensive databases of CPT results. However, due to the time involved in analysing these data, much is left unused. A more reliable impression of the subsurface could result if such CPT data could be incorporated in later ground characterisation studies.
26.2 The INURCS project The aim has been to explore the possibility of using soft computing tools to assist subsurface modelling with CPT data. The intention has been to use as much available CPT data as possible, yet be objective and take cogniscance of uncertainty. The techniques have been developed as part of the INURCS project ("Indelingssystematiek van de Nuenen Groep in Roerdal / Centrale Slenk"), a cooperative venture between TNO-NITG (Netherlands Institute of Applied Geotechnics) and Utrecht University. One of the goals has been to develop a geological model for the upper 30 metres of the subsurface in the Roer Valley Graben area in the southern part of the Netherlands (Figure 26.1). The Roer Valley Graben is a continuously subsiding tectonic basin, within which new sedimentation can take place. Sediments of aeolian, lacustrine and fluvial provenance environments are known, dating from the Middle Pleistocene. Periglacial processes have subsequently disturbed the sediments. As a result, considerable spatial and temporal variations in sedimentary character occur (Schokker, 2001). For this study, 75 new CPTs were conducted (Figure 26.1); the cone resistance (qc) and the friction ratio (R,) were measured every 20 mm. M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 293–300, 2003. c Springer-Verlag Berlin Heidelberg 2003
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Figure 26.1. Map of the study area. The CPTs were conducted along a line marked by the text "Roer Valley rift system"
26.3 CPT interpretation The need to analyse many CPT results in a consistent manner led to the need to develop an automated process to assist interpretation. This would need spatial knowledge concerning the distribution of relevant parameters and knowledge of the geological profile. The approach has been used to automatically segment the CPT sequence using a dynamic programming algorithm proposed by Huijzer (1992), discussed further below. The next step is to parameterise the segment signals, a pre-processing step for fuzzy clustering.
26.4 Automated segmentation of CPT sequences Huijzer's dynamic programming algorithm is based on constrained hierarchical clustering; a 'cluster' is a sequential series of CPT observations. All possible segmentations of a CPT trace are evaluated and the segmentation that minimises the pooled within-group dispersion is identified as being the optimum ('correct') segmentation.
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If the segments are denoted by g and ni is the observation at the boundary between two segments, the pooled within-group dispersion matrix is given by (Huijzer, 1992):
One of the input parameters for this method is the number of segments in the CPT sequence. In most geotechnical investigations, this number will not initially be known. Huijzer (1992) examined several statistical criteria which might be used to determine the number of segments, but found that the results were not geologically relevant. Another approach considered by Huijzer (1992) was to use a fixed number of segments based on the length of the CPT. The risk of such an approach would be that not all the relevant features would be extracted since a relatively short CPT may well contain relatively few relevant features. In the absence of a good statistical method to determine the number of segments in a CPT, a measure based on the CPT length was considered. For the regional survey in the Roer Valley, an average of 1 cluster per 2 metres CPT length was chosen, based on the geologist's expert knowledge. In order to select the most important features, the CPT sequences were then segmented between 2 and 40 each. The most frequently occurring boundaries within each partition levels were then selected. As the vertical size of the extracted feature tends to get smaller with increasing partition level, such a method ensures that all major features, large and small, are selected. This approach worked well, as can be seen in Figures 26.2 and 26.3. Figure 26.2 is akin to how a human expert would segment the sequence Figure 26.3 shows the best segmentation for 24 segments from a statistical viewpoint. The choice of the amount of segments per metre down the CPT depends on the purpose of the CPT investigation and the variability of the geological profile. An average of 1 segment per 2 metres CPT length proved to be a good choice for the purpose of a regional facies analysis for the Roer Valley.
26.5 Segment parameters Once the CPT sequences have been segmented, the resulting segmentation needs to be characterised in such a way that comparison with other segments is possible. This parameterisation is a pre-processing step for the fuzzy clustering procedure which follows.
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A logical approach is to use measures of central tendency: the median of the qc and R, data together with the (relative) interquartile range. The median and range, although useful, are not the only characteristics which a human interpreter would use to compare CPT segments. Some lithological aspects of the subsurface can also be related to the shape of the CPT trace. Coerts (1996) developed other parameters in order to quantify such shapes. These parameters measure the trend of the signal, the concavity of the signal, the number of fluctuations about the median and the number of significant peaks. Coerts also described a 'sharpness of boundary' coefficient, but this parameter was later found not to be helpful to the analysis of fuzzy clustering.
-35
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qc [MPal Figure 26.2. Segmentation of the sequence
Figure 26.3. Segmentation of the sequence for CPT M39: 24 boundaries were selected for CPT M39: 24 boundaries were selected from just the 24 segment partition from 2 to 40 segment partitions
26.6 Fuzzy clustering Having segmented the CPT sequence using Huijzer's algorithm and parameterised it, the next stage was to spatially correlate the CPT segments. This has been done using the fuzzy c-means algorithm and an extension of this, the E-FCM algorithm.
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26.6.1 Traditional fuzzy c-means The fuzzy c-means algorithm (Bezdek, 1973; Hoppner, 1999) is the best-known fuzzy clustering algorithm. There are two problems that have to be solved when performing a fuzzy c-means analysis. One problem is to find the value of c, the number of clusters present in the data set. The other problem concerns the preprocessing of the data. Many measures of cluster validity have been developed to find the optimum number of clusters in a dataset (Bezdek, 1997). Neither the Partition Entropy and Partition Coefficient (Bezdek, 1981) were able to identify more than two clusters no matter which type of pre-processing was done. This is too little for a geotechnical application. In the Roer Valley project, the best results were obtained using the Xie-Beni Separation index (Xie, 1991) and the Roubens' Fuzziness Index (Roubens, 1982). These validity measures both found six clusters in the dataset. Because of the differences in the units of the parameters, it seemed logical to conduct a pre-processing step prior to the fuzzy c-means analysis. Without preprocessing, the algorithm considers parameters with higher standard deviations to be 'more important' than parameters with lower standard deviations. Normalisation of the parameters removes this effect but, in CPT data analysis, the effect is actually desirable. Segments with a low cone resistance and high friction ratio should not belong to the same geotechnical cluster as a segment with a high cone resistance and a low friction ration, no matter how much the trend and the concavity of the traces may look alike. The standard deviation of the m, values is larger than the standard deviations of the group of m , p and r values. The standard deviations of i,ri , t and c values are the smallest and therefore carry the least weight in the fuzzy cluster analysis. The result of running the cluster analysis for the 6 clusters found by the cluster validity measures discussed earlier is depicted in Figure 26.4, and found to be satisfactory. The marker layer in this section, the light grey (clay) layer, is clustered correctly. The clustering result of the other segments also seems reasonable. 26.6.2 Extended fuzzy c-means ('E-FCM') The standard fuzzy c-means algorithm has several drawbacks. Because it tends to iterate towards local minima, a good initialisation is required to arrive at a global minimum. Especially when there are clusters with varying data density and with different volumes, as will be usually the case with geotechnical data, a bad initialisation can easily lead to sub-optimal clustering results. Also, user supervision is desirable to ensure that the correct number of clusters is chosen. Bezdek et al. (1997) revealeds no single 'ultimate' cluster measure. To overcome these difficulties, an algorithm was implemented that uses volume prototypes instead of point prototypes ('cluster centre points') and unsupervised similarity-based cluster merging. This Extended Fuzzy c-means (E-FCM) algorithm was developed by Kaymak et al. (2000). The volume of the prototype is changed dynamically to best fit the data. Thus a dense spot of data points can fall inside a prototype and be fully assigned to a sin-
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gle cluster. The prototypes will not be 'pulled' towards each other because of the partial memberships of the data points. This improves the quality of the clustering process, as shown by Kaymak et al. (2000). The cluster initialisation problem is addressed by a similarity-based cluster merging approach. The algorithm is initialised with a sufficient number of clusters. Then the most similar clusters are merged. The process is repeated until the similarity between the clusters is lower than a certain threshold. This not only deals with the initialisation problem, but also determines the number of clusters dynamically and in an unsupervised way, whilst reducing the computing power required. When the E-FCM algorithm was run on the CPT data, 3 clusters were found, primarily on the basis of the median of the cone resistance. Visual inspection of these clusters showed that the result was logical from a geotechnical viewpoint: the three clusters distinguished loose soils (peat and silty sand), moderately dense sands, and dense sands (see Figure 26.5). The influence of the median of the cone resistance parameter on the clustering is reduced. When the algorithm is run again on these three datasets, a total number of eight sub-clusters were retrieved (Figure 26.6). Because of the heterogeneous ground and the comparatively large CPT spacing (500 metres), the validity of the clustering is not immediately evident. Nevertheless, the clustering seems reasonable. A few marker layers occur and the two-step E-FCM approach identified them correctly; one is indicated in Figure 26.6. The results of the E-FCM analysis are comparable to the established fuzzy cmeans analysis using the Xie-Beni cluster validity measure. However, the E-FCM algorithm is faster, unsupervised, and theoretically more robust.
Figure 26.4. Result of fuzzy 6-means clustering of CPT segments from CPTs M39 to M45, described by 12 parameters. In light grey (at -20 m depth) is a marker layer which was correctly classified
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Figure 26.5. Result of runing the E-FCM algorithm on the CPT data, showing three clusters depth I~II NAP]
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Figure 26.6. Running the E-FCM algorithm on data for three clusters, resulting in eight sub-clusters. Between the arrows lies one of the marker layers that had been correctly classified (c.5 Figure 26.4)
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26.7 Conclusions Although the two-step E-FCM algorithm does not on its own constitute a 2D subsurface model, the results are sufficiently encouraging to conclude that geotechnically useful semi-automated subsurface modelling of CPT data is feasible. The usefulness of the results will be highly dependant on the heterogeneity of the ground profile and the scale of the CPT investigation programme. The method is fast; the CPT dataset is large, yet can be processed within a day on a PC. Although the method has yet to be fully optimised, it offers considerable promise at reducing the time needed to model the ground during a large site investigation project.
Acknowledgements The authors would like to thank Jeroen Schokker at TNO-NITG for his co-operation and hls willingness to share his expert geological knowledge of the study area. Furthermore, the authors would like to thank TNO-NITG for kindly supplying the data used in this project.
References Bezdek JC (1973) Fuzzy Mathematics in pattern classifications, Ph.D. thesis, Applied Math. Center, Cornell University, Ithaca. (unpublished) Bezdek JC (1981) Pattern recognition with fuzzy objective algorithms. Plenum Press, NY Bezdek JC (1997) A geometric approach to cluster validity, Soft Computing, 1, 166 - 179 httu://link.springer.de/linMservice/_i oumals/OO5OO/~apers/7OO 1O O O O 10 166.pdf Coerts A (1996) Analysis of static cone penetration test data for subsurface modelling. PhD dissertation, Universiteit Utrecht, The Netherlands. (unpublished) Hoppner F (1999) Fuzzy cluster analysis: methods for classification, data analysis, and image recognition, John Wiley and Sons, USA Huijzer GP (1992) Quantitative Penetrostratigraphic Classification. PhD dissertation, Vrije Universiteit Amsterdam, The Netherlands], Copyprint 2000, Enschede. (unpublished) Kaymak U, Setnes M (2000) Extended Fuzzy Clustering Algorithms, Rotterdam School of Management, URL: www.eur.nl/WebDOC/doc/erirn/erirnrs2000 1123094510.pdf Roubens M (1982) Fuzzy clustering algorithms and their cluster validity, European Joumal of Operational Research, 10,294-301 Schokker J (2001). Palaeogeography and facies architecture of the Middle- and LatePleistocene Nuenen Subgroup deposits in the Roer Valley Graben and its implications for applied use. PhD dissertation, Universiteit Utrecht, The Netherlands. (unpublished) Xie XL, Beni G (1993) A validity measure forfuzzy clustering, IEEE Trans. on Fuzzy Systems, 1, 69-78
27 Impact of geoinformatics on the emerging Geoscience Knowledge Integration Paradigm Frank D' Agnese, Grady O'Brien U.S. Geological Survey, Tucson, Arizona, USA
27.1 Introduction The advent of computers has provided valuable tools for the geoscience community to use in addressing societal concerns. Geoscientists have become accustomed to using vast data and information bases to conduct complex analyses. For the last twenty years, numerous investigations have implemented and evaluated various techniques for integrating data and information. However, the full potential of data, information, and knowledge integration has not yet been realised. As the geoscience community moves into the 21" century, there will be a clear paradigm shift regarding how geoscientists do their work and interact with each other. In the future, geoscientific investigations will shift from a more passive role of data analysis and information management to a more active role that seeks to create, integrate, and transfer knowledge in ways that are responsive to social, political, and economic needs. The emerging discipline of "Geoinformatics" provides the appropriate base for such integration. Geoinformatics address the growing need for utilising multidisciplinary geoscience data to understand complex Earth systems. Applying the geoinformatics concept demands a new paradigm that includes not only the more traditional technological issues of geoscience data collection, storage, retrieval, and processing, but also new tasks. These include the formulation of essential data, information, and knowledge models, the formalisation of "best-practice" workflows, and ultimately the application of knowledge management and decision support. This "Geoscience Knowledge Integration Paradigm" is a practical extension of the data and information integration achievements of the late 201h century. This paradigm is a prerequisite for augmenting our ability to develop and deliver timely and cost-effective solutions to societal concerns. The full implementation of this paradigm will ultimately require the geoscience community to realign organisational structures.
M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 303–312, 2003. c Springer-Verlag Berlin Heidelberg 2003
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27.2 Addressing society's emerging concerns The Geoscience Knowledge Integration Paradigm is emerging at a time when many within the geoscience community realise that their historical and accustomed role of providing data and information is no longer sufficient, because society is now demanding knowledge and wisdom. Realignment and integration of geoscience research and applications to more effectively address society's emerging concerns is an important discussion topic within many public geoscience organisations. The following excerpts are but a small sample of these discussions and recommendations: "In the future, information (and knowledge) management at the USGS should shift from a more passive role of study and analysis to one that seeks to convey information (and knowledge) actively in ways that are responsive to social, political, and economic needs." (National Research Council, 2001) "The challenges and opportunities ahead will demand closer relationships between the British Geological Survey and all of its stakeholders." (Natural Environment Research Council, 1999) "Without mastering geoscientific knowledge management, geoscientific research would remain on a relative low grade of 'information processing'." (Netherlands Institute of Applied Geoscience TNO-NITG Annual Report, 2000) These discussions emphasise the evolution of these organisations from agencies that are organised predominantly to make discoveries, and convey to society what they have discovered, to agencies that are organised to understand how the earth works and convey to society how best to cope with the realities of interacting with the Earth (NRC, 2001). There is increasing pressure on these organisations to not only provide basic data and information, but also to explicitly package and disseminate knowledge for use by society with informed decision-making. The result is an increased demand for user-specific solutions that contain a clearly documented level of uncertainty. Solutions connected with explicit levels of uncertainty may be used by society to make operational decisions. Further, society requires that the geoscience community offer expert insight to guide them in making judgements about future risks and consequences (Konikow and Bredehoeft, 1992).
27.3 From data to wisdom The geoscience community integrates basic data and applies scientific expertise to provide knowledge and wisdom. The relationships and characteristics of data, information, knowledge, and wisdom are summarised in Table 27.1. Existing datamanagement methods do not readily support this progression from data acquisition
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to scientific information and then to knowledge and, ultimately, to scientific wisdom by geoscientists. Table 27.1. Definition of basic terminology
Term
Definition
Data
Simply structured records of transactions
Information
Adding meaning and context to data with the intent of influencing the perception of a receiver
Knowledge
A fluid mix of experience and contextual information that provides a framework for evaluating and incorporating new experience and information
Wisdom
Enabling wise decision-making by applying knowledge, values. and exDert insight
The integrating progression from data to wisdom can be illustrated through the use of a pyramid (Figure 27.1). Data and information form the foundation of the process and are maintained by, and contained within, the geographical information systems, technical databases, warehouses, and archives of an organisation or community. For example, geoscientists develop a geological framework, which is a conceptual model of the observed system, using acquired data, information, and their understanding of natural processes. Currently, this process is conducted using workflows developed by individuals involved with a specific study. The geological framework may be subsequently abstracted into process andlor predictive models that provide a better understanding of the natural phenomenon that is being observed. Such understanding produces knowledge and this knowledge, when further managed and applied to provide insight for societal decision-making, is manifested as wisdom. Existing geoscientific investigations generally do not adequately capture or enable the formal process of knowledge and wisdom creation. The process and predictive models developed by geoscientists are the knowledge-based requirements of 'decision analysis'. These models include sensitivity, uncertainty, and predictive analyses that are provided in a probabilistic framework. Such analyses provide a basis for wise decision-making (TNO-NITG, 2000). While the goal of many geoscientists and geoscience organisations is to provide relevant 'decision information' that can support the decision analysis process, they are limited by their incomplete understanding of the decision process (Turner, this volume).
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Decision lnformation Knowledge Management and Application Process Models, Predictive Models, Workflow Management, Protocols Geographic lnformation Systems, Data Archives, Data Warehouse, National Databases
Information Data
.
The Geoscience Knowledge Integration Paradigm
Figure 27.1. The Geoscience Knowledge Integration Paradigm (modified from TNONITG, 2000)
27.4 The need for a Geolnfrastructure A geoscience information infrastructure is needed to facilitate the dynamic, bidirectional flow of data, information, and knowledge. This 'GeoInfrastructure' is constructed upon the technological hardware and software of the information age and so includes advanced analytical and visualisation tools fully supported by databases, data warehouses, and data-management systems. It also requires technological advancements in the development and adaptation of data, information, and knowledge models to provide better structuring of data, information, and knowledge during geoscience investigations. The emerging field of geoinformatics provides a vehicle for creating the desired 'GeoInfrastructure' and enabling geoscience-knowledge integration. At the present time geoinformatics is concerned with the collection, storage, retrieval, processing, and dissemination of geoscience data, information, and knowledge. As geoinformatics matures it will enhance the active flow of data and information to, and from, process models and decision processes, which will facilitate geoscience knowledge creation. Full implementation of this new GeoInfrastructure allows knowledge to be captured and directed to decision processes. As geoinformatics broadens, those implementing it are recognising that individual technological advancements are not the only requirement for improved data, information, and knowledge management. To be fully successful, the implemented GeoInfrastructure must enable scientists to maintain their creativity and intellectual freedom while working within basic operational guidelines. Changes in the way in which scientists conduct their investigations may be necessary. Most scientists will likely embrace this paradigm and its new workflows once they realise that it improves the quality of their science and frees up their time and energy to allow for more and better scientific discovery.
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27.4.1 Integrative protocols Because computer technology alone cannot enable geoscience knowledge integration, the GeoInfrastructure must contain integrated, formal, and fully documented protocols. Protocols, which are sometimes called 'business processes', are a collection of activities that integrate various inputs to create a measurable output. The customer measures the value of the output (Nasser et al., this volume). In the case of the applied geosciences, the ultimate customer is society. Protocols can be considered to be rules that direct and facilitate work processes. They vary from simple operational tasks to complex, iterative modelling scenarios. An operational protocol might involve rules that establish how data are saved and formatted, how sets of attributes are predefined, and how data are displayed and visualised. Complex protocols may define how process models feed decision processes, and how decision process results will be used to modify model simulations. These protocols and their embedded rules allow rapid modification and replication of analyses and models, and also facilitate the integration of these analytical results into decision processes.
27.4.2 Enabling geoscientists The geoscience-knowledge integration protocols cannot focus only on data and information. These protocols must also 'enable' the geoscientists. Individuals and teams of scientists must be provided with the appropriate tools and procedures to ensure integration of scientific context, expertise, intuition, and tacit knowledge. This requirement dictates that geoscience organisations, and geoscientists themselves, change their mode of operation. Effective utilisation of these protocols requires deliberate cultivation of individual professional behaviour so as to promote improved interaction. From an organisational viewpoint, new structures and metr i c ~must be instituted that encourage this interaction and provide appropriate rewards for positively responding to them.
27.4.3 Geolnfrastructure Value The value of the GeoInfrastructure will be fully realised when it is demonstrated that it can free-up thousands of hours for geoscientists to work more productively by creating new, wide-ranging efficiencies. DYAngeloand Troy (2000), for example, estimated that: (1) Geoscientists spend 30-70 percent of their time looking for, extracting, reformatting, validating, cleaning up, importing, and documenting data; (2) Organisations frequently acquire or re-purchase identical data many times, simply because it is faster and cheaper to re-acquire data from vendors than to search internally for the same, already purchased, data that had been subsequently "lost;" and
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(3) Up to 40 percent of data acquired by the average operating organisation over the last 10-15 years is missing, corrupted, or otherwise unusable (e.g. stored on obsolete media).
The value of the GeoInfrastructure also lies in the increased time available for actually integrating ideas and tacit knowledge. Once the GeoInfrastructure has been implemented, the emphasis within the integrated "geoscience knowledge organisation" can then be placed on developing a better understanding of natural processes and alternative hypotheses instead of in data manipulation.
27.4.4 Implementation of the Geolnfrastructure The implementation of the described GeoInfrastructure thus involves a conscious decision to commit an organisation's resources - both people and technology are integral participants. The effectiveness of a geoscience organisation in utilising the "Geoscience Knowledge Integration Paradigm" depends on its willingness and ability to transform its in-place data-management system. The ramifications of this transformation can be understood by reference to the data management maturity model described by D'Angelo and Troy (2000) (see Table 27.2). Using this model they suggest that organisations can assess their current position, from 'base' to 'fully-optimised', in terms of data, information, and knowledge management capabilities and organisational maturity. Because it is common for organisations to overestimate their data management maturity level, accurate assessment requires an honest, unbiased evaluation. Implementation of the Geoscience Knowledge Integration Paradigm will require organisations and the geoscience community to develop protocols for knowledge integration that enable growth in data-management maturity, so that the full potential of integrated data management can be realised. The GeoInfrastructure will tie the protocols together and facilitate increased data-management maturity. This evolution is based heavily on operational and organisational realignments. Organizational alignment, as it relates to this GeoInfrastructure, does not change an organisation's core business. Instead, it creates and communicates the necessary changes in vision, work processes, and employee-performance standards required to derive full value from a knowledge-management system. The organisation must fully embrace the new paradigm; it must commit both significant resources and strong management support to realign the organisation to be consistent with "bestpractices" and protocols. When this happens, organisations can realise the maximum return on major technological and employee investments. Without management commitment, support, and attention to the necessary changes in processes and employee performance management systems, only a fraction of the potential value can be realised. Finally, both line-management and users must "own" the organisational alignment effort. When line managers understand that new work processes are required, they can engage their staffs for solutions that minimise impact on employees (D' Angelo and Troy, 2000).
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Table 27.2. Data-management maturity (modified from D'Angelo & Troy, 2000) Maturity Level (I-V) V). Fullv O~timised: Exploration success rate is close to the development success rate IV) Predictable Risk: Ability to routinely reduce uncertainty & data-related risk -
m
Process Performance Processes are almost entirely automated
Technology Support Data mining expert systems
Statisticallystable processes routinely measured against industry standard performance metrics Standard, consistent, statistically capable, measurable processes; standardised process performance metrics begin to evolve
Automation and background performance of processes/tasks: automated decision support system Integrated technology designed to enable emerging "best practice" processes; technology suppliers are partners in defining how technology achieves the best results
11) Managed: Standardised tasks and roles; advanced technology introduction begins
Individuals typically develop & follow a process that "works for them;" processes are not common between individuals or across locations
I) Base: Capable people
No defined processes; individual performers may follow a different process each time
Non-integrated point solutions designed for specific tasks; individuals have primary responsibility to figure out how to integrate & use technology to achieve results None or general purpose tools (e.g. Excel, Access); data management is a mostly "personal" function, not a corporate one
111) Corporate Competency: Capabilities are institutionalisedwithin the company; enabled by mature technology
& heroic efforts
Quality and Predictability of Results Almost com~letecertainty of results is achieved
Value Determination
The reliability and predictability of results is significantly improved (i.e. six-sigma vs. three-sigma) Good quality results within the specified tolerances most of the time; poorest individual performers improve towards best performers; more leverage achieved on best performers Variable quality with some predictability; the best individual performers can be put on "business critical" projects to reduce risk and improve results
Lower return on investments in data management is accepted in exchange for reduced risks Measurable; able to recognise costs and benefits; perform cost-benefit analyses and maximise return on investment; more good results faster, with fewer people Anecdotal; based on the capabilities of individual performers & specific memorable events
Total corporate dependence on individuals; little to no corporate visibility into data management cost or performance; variable quality; low predictability & repeatability of results
Subjective; "gut feel" for performance, costs & value received
27.5 The development of a 'GeoPro' structure Implementing a GeoInfrastructure within an organisation will provide increased efficiency in the evolution of geoscience knowledge. However, the implementation requires a controlling, or supervisory, protocol that enables management of the requests of geoscientists and society alike. A team of geoscientists within the U.S. Geological Survey charged with conducting a series of hydrogeological characterisation studies has begun to develop such a protocol. Their studies are concerned with the availability and sustainability of groundwater resources within the desert south-western United States. The large geographical area and the multidisciplinary nature of their studies required the collaboration of many individual geoscientists located in diverse locations. Project managers recognised the need to establish standards and procedures to facilitate collaboration and exchanges so they developed a protocol. They defined their protocol as the "Geoscience Knowledge Integration Protocol" or "GeoPro". GeoPro is based on a collection of integrated protocols that encompass the entire geoscience business process (Figure 27.2). Several fundamental work processes involved in typical geoscience investigations have been identified and form the basis for the GeoPro structure. GeoPro contains five major components that are shown in Figure 27.2:
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Technical Core Risk Assessment and Decision Support Workflow Management Information Technology Infrastructure Dissemination
The technical core contains linked sub-processes that enable integration and management of geoscience data, three-dimensional geological models, and numerical process and predictive models. The technical core is fundamentally designed and structured to provide the tools and procedures required by the management, analysis, and evaluation of the uncertainty inherent in geoscience data, information, and knowledge. As illustrated in Figure 27.2, the Technical Core includes three major functions - data management, framework modelling, and process and predictive modelling. Data management includes data collection and data analysis processes. Datacollection processes involve obtaining new field data as well as acquiring existing data in a variety of formats and structures. Data analysis processes handle how data are selected, perform data quality assessments, facilitates data interpretation, and generation and storage of derived data. Framework and process modelling cover a wide spectrum of discipline-specific processes, but there are commonalities across the disciplines. Creation of geoscience models of the subsurface involves conceptualisation, preparation of data sets, and definition of model geometry and constraints. These factors, together with determination of initial conditions, property uncertainty, parameter sensitivities, calibration, evaluation, and predictive scenarios are all critical work processes that must be managed during process and predictive modelling. This technical core is supported by decision-analysis tools within the Risk Assessment and Decision Process component, and these are in turn supported by the workflowmanagement capabilities within the Workflow Management component. Risk assessment and decision processes must be integrated throughout the investigation. Thus the Risk Assessment and Decision Process component is shown as surrounding and supporting the Technical Core in Figure 27.2. Application of risk assessment and decision processes at multiple stages during investigations helps to ensure that the studies answer the societal questions being posed. The Workflow Management component guides how the work is performed. It controls the data and information flows within the Technical Core and Risk Assessment and Decision Process components. It also manages and maintains the integrity of the documents (reports, graphs, tables, maps and models) produced during an analysis. It thus has links to the Dissemination component. The Information Technology Infrastructure component supports processes for managing system security and user authentication, database administration, database connectivity, system configuration, and disaster recovery. Dissemination of the data, information, and knowledge created by the study is a critical component for demonstrating the value of any geoscientific investigation. The Dissemination component controls the product and information flow. It includes the creation of appropriate metadata descriptions and provides the appropriate versions of reports or data, according to the needs of the requestor.
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Risk Assessment & Decision Processes I
V
Information-Technology Infrastructure Figure 27.2. Components of the Geoscience Knowledge Integration Protocol (GeoPro)
27.6 The need for standards The immediacy of societal concerns dictated the need for GeoPro. Unfortunately, this hindered development and implementation of the protocol. The rapid response demanded by society contributes to the short-lived nature of many applied geoscience investigations and are not conducive to good development of standards. Yet, universally accepted standards enable individuals, organisations, and the entire geoscience community to interact in truly integrated ways that are not currently possible. For example, without standards, the simple task of exchanging data becomes complicated and time consuming, and the data are often inadvertently corrupted. When these difficulties are encountered, further interaction is discouraged and the perceived value of exchanging data and ideas is reduced. Scientists are thus likely return to their isolated work environments and continue to develop their products independently. Only when a client must be provided with a result will the work of numerous independent investigations be 'integrated' into a
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unified product from which a solution to a societal concern can be derived. Unfortunately, the independently developed products often may not agree in content, quality, or format and meaningful integration is either difficult and timeconsuming, or impossible. Standards will provide the framework within which these independent studies can be based and can enable better integration of results. Standards will also enable the geoscience knowledge integration protocol to evolve into a stable and secure structure capable of providing accurate and consistent assessments of the quality of geoscience knowledge that are required by society. To be useful in providing competent and prudent solutions, the integrity of the data, information, and knowledge must be preserved. Likewise, maximum impact and acceptance of the protocol demands clearly documented and comprehensive sensitivity and uncertainty analyses be related to and preserved for all data, process, and predictive models. Ultimately, without standards, the protocol cannot be widely implemented and accepted, nor can the paradigm be realised.
27.7 Conclusions Recent efforts conducted as part hydrogeological characterisation studies concerning ground-water availability and sustainability in the desert south-western United States have encouraged the development of a prototype protocol. This effort demonstrates that it is entirely appropriate for the entire geoscience community (public, private, and academic) to engage in a co-ordinated effort to capture and improve elements of this emerging protocol. By sharing experiences, a stronger, more robust, protocol can be developed and tested in a greater variety of operational environments. Also, no single geoscience organisation has the financial resources or knowledge base necessary to rapidly develop a complete protocol. By sharing experiences and responsibilities, a more effective protocol and a more closely aligned geoscience community will emerge.
References D' Angelo J, Troy R (2000) Data Management Helps Create Value. In: The American Oil & Gas Reporter New Products and Technology Issue: Data Management Leverages Technology, Enhances Success, July 2000,3 pp. Nasser K, Bolivar S, Canepa J, Dorries A (2003) New paradigms for geoscience information management [Chapter 4, this volume] Natural Environment Research Council (1999) Foundations for a sustainable future: A new strategy for the British Geological Survey, Nottingham, UK, 12 pp. National Research Council (2001) Future Roles and Opportunities for the U.S. Geological Survey, National Academy Press, Washington, D.C., 152 pp. Netherlands Institute of Applied Geoscience TNO-National Geological Survey (TNONITG) (2000) Geo Information and Knowledge Management (GKM), 4 pp. Turner AK (2003) Definition of the modelling technologies. [Chapter 3, this volume]
28 Assessment of uncertainties in volume and yield estimations of clastic deposits Brecht Wassing, Hans Veldkarnp, Chris Bremmer Netherlands Institute of Applied Geoscience (TNO), Delft, The Netherlands
28.1 Introduction In the Province of Limburg, in the southern part of the Netherlands, land adjacent to the Maas river is regularly flooded during peak discharge. To prevent future flooding, several measures are being considered. Amongst these are the construction of higher quays and embankments, dredging and excavation, the intention being to deepen and widen the river channels. Dredging and excavation will produce an additional benefit, yielding sand and gravel deposits which can be used as construction aggregate. The economic yield from the exploitation of these sand and gravel deposits can be used to offset finance for the flood control works. To optimally plan excavation and dredging works and obtain a good insight to the financial risk of the project, a thorough insight to the structure of the ground is required. The study focussed on two pilot sites and considered three issues: 1. Global estimation: Methodology to obtain estimates of total volume, tonnage and mean grading of the sand and gravel resources. Estimation should preferably utilise all available data from the site investigations. 2. Financial risk: How to assess uncertainties associated with estimates of total volume, tonnage and mean grading. 3. Local estimation: Optimal planning of dredging and excavation works based on local conditions, e.g. depth to a gravel layer, the lithological composition of the layer, and related uncertainties.
28.2 Site investigation To establish the ground profile along the river a site investigation was conducted, extending from the most southern (Dutch) part of the river to the city of 's Hertogenbosch in the Province of Noord-Brabant. The site investigation was carried out under the auspices of Rijkswaterstaat, projectbureau Maaswerken. Various types of data were gathered: from shallow boreholes located to establish the thickness and degree of contamination of the superficial clay layer covering the sand and gravel deposits; disturbed and undisturbed sampling from deeper boreholes, toM.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 313–322, 2003. c Springer-Verlag Berlin Heidelberg 2003
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gether with cone penetration tests, to assess the depth, composition and extent of the workable sand and gravel deposits. These boreholes were also used to find the depth of the so called 'substratum', a marine deposit known to lie under the coarse sands and gravels, and likely to consist of fine sands. The top of this substratum forms the bottom of the potentially workable deposits. Dredging works preferably should not extend into this fine-grained material. Geophysical measurements such as shallow seismics (on water) and electrical resistivity (on land as well as on the Maas) were performed in order to determine the lateral extent and continuity of the workable deposits and their internal structure. Boreholes samples were used for laboratory particle size analysis and other physical and chemical properties (such as the presence of materials like wood and flint), and to determine the suitability of sediments for use as aggregates for concrete, mortar and other applications.
28.3 Results of the site investigation 28.3.1 General geology of the Maas deposits The river sediments were deposited during the Late Pleistocene (Weichselian) and belong to the so called Veghel Formation. These consists mainly of coarse grained sediments typical of a braided river system. In general these braided river sediments contain channel deposits as well as bars (both comprising gravels and coarse sands) and overbank deposits (i.e. horizontally layered fine sands, silts and clays). However, the Veghel Formation contains few overbank deposits in the area where the dredging is to be conducted. The Holocene Maas deposits consist of channel deposits (varying from coarse gravels to fine sands) and fine grained floodplain deposits, laid down in a meandering river system (the Betuwe Formation). During the Holocene, the freely meandering Maas river was able to rework part of the older Pleistocene sediment sequence.
28.3.2 Geology of the southern and northern pilot sites The river sediments of the Veghel Formation likely to be workable for aggregate in the southern pilot site belong to a more proximal facies than those encountered within the northern site. Borehole logs indicate that sediments in the southern area consist mainly of coarse to moderately coarse gravels and boulders, whereas the sediments in the northern area are moderately coarse to fine gravels, alternating with coarse gravelly sand layers. Considering the lithologies, the potentially workable river sediments of the southern site can be regarded as more homogeneous than the northern area. Figure 28.1 shows two schematic cross sections through both sites.
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Within both sites, a fine-grained cover of clay and silt is found on top of the coarse grained sediments (the Betuwe Formation). This layer can reach a thickness of several metres. The bottom of this layer forms the top of the workable deposits.
Figure 28.1. Schematic geological cross section through the southern pilot site
Below the coarse river deposits of the Veghel Formation at both sites, the marine fine grained sands of the Breda Formation are distributed; these occasionally contain glauconite grains. Due to its chemical and fine-grained composition this deposit is not suitable as a construction material and so this formation forms the substrate of the workable deposits. On both sites several river terraces can be discerned, as distinct geomorphological edges and as a discontinuity at the bottom of the workable deposits (profile LL' in Figure 28.1). Through the centre of the southern site runs a NW-SE striking fault: the Breuk van Heerlerheide. However, the offset of this fault does not influence the position of the workable deposit. The northern site is bounded by a NNW-SSE striking fault system: the Peelrandbreuk.
28.3.3 Sources of inaccuracy of data The information concerning local geology, depth of layers and the lithological composition of each layer was determined from a variety of sources, such as boreholes, cone penetration tests, electrical resistivity and shallow seismics. These various sources of information have varying degrees of accuracy (Table 28.1). For example depths of distinct lithological layers was obtained from several drilling techniques and sampling strategies (disturbedhndisturbed). As a consequence, ac-
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curacy of depth determination will differ. Geophysical measurements will yield depths too, but their accuracy will depend strongly on the quality of the interpretation (i.e. the method of interpretation and the ability to correlate with nearby boreholes). The accuracy is related to the type of data, and needs to be taken into account when estimating depths of layers and volume, tonnage or mean grading of resources. The ranges used in this study to take into account the accuracy of depth determined by the various methods are given in Table 28.1. Table 28.1. Error ranges used in this study applicable to depth Data source Borehole logs
+I- 0.25m
Ranges used in this study
CPT logs
n/d
Electrical resistivity
+/- 0.5m for shallow layers
(top workable deposit)
Remarks Usually considered as hard data Usually considered as hard data Always considered as soft data
+/- 1.Om for deeper layers
Seismic reflection
(bottom workable deposit) +I- 0.25m
Always considered as soft data Only distinct reflectors are
28.4 Geostatistics for estimating volume and grading Geostatistical methods are able to take into account the parameter distribution and spatial variability. Two principal geostatistical tools are applicable: kriging and simulation. For the present application, kriging has some major disadvantages: only a best estimate of depth can be obtained, thus giving a smoothed image of the surface. The individual realisations resulting from stochastic simulation are better suited since they echo the original variability, and thus give a better representation of the roughness of the surface. A number of simulation techniques are available. Of these, sequential indicator simulation (SIS) is best suited since it takes into account the data values as well as the data configuration when estimating uncertainty. A full explanation of SIS is beyond the scope of this paper, but is published by Govaerts (1997). The sequential indicator simulation is achieved by transforming the original continuous data values into indicator values at specified cut-off thresholds. These cut-offs are based on the original cumulative distribution function (cdf) of the dataset. For every cut-off threshold a separate variogram is constructed. The indicator approach has as a major advantage that one can model different spatial correlation structures for each separate cut-off value. The pattern of spatial
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relation structures for each separate cut-off value. The pattern of spatial continuity may differ, depending on whether the attribute is small, medium or large. The sequential indicator technique can thus model different spatial structures and different continuities for varying cut-offs. Thus 'extreme' structures, such as deep channels, can be modelled as continuous features. The indicator technique can handle constraint intervals. Soft data can be transformed into indicator values, thereby considering the varying degrees of accuracy for the different sources of information. The first step in the estimation of total volumes the depth intervals of the workable deposit in all above data types were identified. In both areas the top of the 'substrate' (top of the Breda Formation) and the bottom of the clay cover (Betuwe Formation) were interpreted in boreholes, CPT's and the results of the geoelectrical and seismic measurements. Based on this information 2D-bounding surfaces (top and bottom) of the coarse grained workable unit were modelled. A priori knowledge of the local geological features, such as the presence of the river terraces and the presence of a major fault in the northern area, was taken into account during the modelling and used to group the data into subsets. As a next step the statistics of these datasets, such as the distribution of depths (represented by the histogram) and the spatial correlation structure of the data (represented by the variogram) was analysed. Special attention was given to outliers, which were omitted or corrected when necessary. Table 28.2 gives an example of the indicator transformation for the 9 cut-offvalues for the top of the workable deposit in the pilot sites, thought from electrical resistivity to lie between 3 1 and 32 m. Table 28.2. Indicator transformation for the top of the workable deposits Proportion
Cut-off
PI0 P20 P30 P40 P50
30.00 30.60 31.05 3 1.42 31.71
Indicator value 1 1
? + -9999 ? -9999
*
Proportion
Cut-off
P60 P70 P80 P90
32.21 32.44 32.83 33.26
Indicator value 0 0 0 0
?a-9999
The sequential indicator simulations were performed using the module SISIM of the GSLIB software (Deutsch and Journel, 1992). The simulations resulted in 50 realisations of both the upper and lower bounding surfaces; all 50 outcomes were equally probable. For both surfaces the 50 realisations were then combined to obtain the mean value for the depth of the top and bottom of the workable deposits at each selected grid point. The standard error of estimation of the 50 simulated values provides a measure of the uncertainty at that specific point. Means and standard deviations of the top and bottom surfaces of the workable deposits will give sufficient local information: the estimated depth of the deposits at a particular location and a measure of local uncertainty.
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Fifty realisations of the total volume which fall within the planned excavations were calculated by subtracting the bottom from the top for all 50 pairs of realisations. The histogram and cumulative distribution function (cdf) of these 50 volumes give insight in the total volume expected to be recovered and a measure of the uncertainties of these estimates. At this stage however the composition and mean grading of this total volume is still unknown. Results of the volumes obtained by the sequential indicator technique were compared with those from the analysis based on a random sample of boreholes. In this random sample the thickness of the workable deposit was determined and averaged. The mean thickness of all boreholes was multiplied by the surface of the excavation, which resulted in an estimate of the total volume. The standard error of estimation for thickness in the boreholes was used to calculate the standard error of the mean - which in itself is a measure of the accuracy of the estimate of the total volume. Table 28.3 gives a comparison of the results of both methods. From these results it appears that when only global estimates of volumes are required the random sampling technique is an efficient tool to obtain these estimates. As opposed to the indicator technique however, the random sampling technique will not give local information on depths of layers and related uncertainties. The internal lithological structure of the workable deposits strongly determines the mean grading and ultimate economic yield. The northern pilot site showed a stronger lateral variation of lithology than the southern site. Therefore the categorical sequential indicator simulation was chosen for modelling the internal distribution of gravel, sand and clay within the unit to be worked. Table 28.4 lists the classes which were used to categorise the lithologies. The stochastic simulations produced 50 equally probable realisations of the spatial distribution of the lithologies (Figure 28.5).
"9-
Figure 28.2. Cumulative distribution function for total volume at the northern site
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Figure 28.4. Horizontal slice through two 3D realisations of the lithologies within the northern pilot site
The coarse deposit in the southern pilot site consisted mainly of gravel and was far more homogeneous than the northern site. The extra effort required to undertake 3D simulations of the lithology promised little reward and so tonnage and mean grading were obtained directly from the volumes calculated in the first step and the (one) data set of grain size distribution curves for the gravel layer. Combining the results of all 50 realisations for the southern and northern sites resulted in a graph with total tonnage per individual sieve and mean grading for the complete excavation (Figure 28.5). Again, the results of the sequential indicator simulation were compared with the results of a simple random sampling technique (Table 28.5). This time the outcome of the random sampling technique differed substantially from the outcome of the indicator simulations. This difference can be partly explained by the fact that the indicator approach required a regularisation of the sample intervals. In the random sampling the original sample sizes were not taken into account.
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Figure 28.5. Global estimates for mean grading and tonnage with respect to sieve size at the northern site Table 28.3. Comparing global estimates using indicator simulation and random sampling
Estimate of total volume Variation coefficient
Indicator approach 1.6e6m3 2.3%
Random sampling 1.7e6m3 10.6%
Table 28.4. Lithological categories used in the sequential indicator approach Class I I1 I11
IV V
Category Clay Fine to very fine sands, clayey sands and sandy clays Moderately fine to moderately coarse sands Coarse to very coarse gravelly sands Gravel
Table 28.5. Comparing global estimates by indicator simulation with random sampling at the southern site Mean of tonnage on 2 mm sieve Variation coefficient
Indicator approach 3.48e6 tons 2.7%
Random sampling 3.10e6 tons 9.0%
28.5 Conclusions Global estimates of total volume, tonnage and mean grading for potentially workable sand and gravel deposits were obtained by the use of sequential indicator simulation. The individual realisations resulting from the simulation procedure could be used to obtain local information, such as the depth of a certain layer at a specific location together with related uncertainties regarding the estimate. Com-
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pared to other geostatistical techniques, sequential indicator simulation offers a straightforward method for handling soft data. It is a powerful modelling tool where spatial continuities of a variety of different geological features like deep channels need to be considered.
References Deutsch CV, Journel AG (1992) GSLIB, Geostatistical Software Library and User's Guide: New York, Oxford University Press, 340 pp. Goovaerts P (1997) Geostatistics for Natural Resources Evaluation. New York Oxford University Press, 477 pp.
29 Uncertainty with characterisation of soils Paulo Coelho Universidade de Coimbra, Portugal
29.1 The Context The geotechnical characterisation of natural soils can be troublesome. Their behaviour strongly depends on several factors that are difficult to control and their properties usually present a variability that must be taken into account. This is controlled by their geological history, notably the conditions existing during and after the last glacial period and the associated sea level fluctuations. During the peak of the last glacial period, sea level was about 120 m below its current position. The adjustment of the drainage system to these conditions strongly increased erosive action on the continents, and rivers excavated deep valleys. Since that time, the slow but sustained rise of sea level intensified sedimentation within the previously eroded valleys, typically enriched with organic matter.
29.2 Geotechnical features of natural soils The associated geotechnical characteristics of such soils may be illustrated in Figure 29.1, showing the effects of consolidation and desiccation. Considerable scatter of o', and OCR values can be expected in the superficial desiccated crust, which is accompanied by scatter in several other (related) physical properties. Another relevant feature can be deduced from Figure 29.2, illustrating variations in depth of several physical properties at the Bothkennar site (UK) (Hight et al., 1992). These physical properties possess a distinctive variability, characteristic of the deposit and not merely a consequence of insufficient quality of the testing techniques. These authors concluded that the natural variability usually found in soils such as the Bothkennar deposit, is mainly determined by the presence of organic matter.
M.S. Rosenbaum and A.K. Turner (Eds.): LNES 99, pp. 323–329, 2003. c Springer-Verlag Berlin Heidelberg 2003
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overconsolidated soil
- -----------
normally consolidated soil
Figure 29.1. o ' and ~ OCR profiles in depth resulting from desiccation (Coelho, 2000) Water content: % 50
.
25
'02
LI
Water content: %
75
50
r
L2 Lava, samples Piston samples S1 a S3 Sherbrooke samples
4 ) - 0
.
I v A
Oxford University *-I City University Glasgow University
.
o
0
(b)
75
Surrey University ERE (on site) Brlstol Polytechnic Bristol University
Bulk density: Mglrn:'
1.6 1.8 I ----I 7 -
I
City University
0 Oxford University
v Bristd Polvtechnic a On site rn~asurementsby 0 Surrey University Imperial College + Ground Engineering f Lloyd (1989) plston samples
-
ek€
Figure 29.2. Variation in some physical properties with depth at Bothkennar: (a) on-site measurements of water content; (b) on-site and off-site measurements of water content; (c) bulk density (Hight et al., 1992)
Figures 29.3 and 29.4 also express the influence that organic matter can have on soil physical properties, Figure 29.3 illustrating the significant reduction of specific gravity that occurs in the presence of organic matter, as measured by Haan et al. (1994). Figure 29.4 reveals the dependency of soil water content on organic content. Other influential factors include the existence of shell fragments from marine organisms and cement, notably gypsum and calcite. Sampling is difficult in soft
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soils, generally requiring specialist equipment and techniques in order to obtain good quality, intact samples.
, 0
0.2
0.4
0.6
Peat-type soils
0.8
Organic content (OM)
Organic content (OM)
Figure 29.3. Variation of G with organic Figure 29.4. Variation of water content (w) content (Haan et al., 1994) with organic content (OM) (Amaryan, 1993)
29.3 Case study: Baixo Mondego The soil at Baixo Mondego, covering the alluvial plain of the river Mondego, is one of the most significant deposits of soft soil existing in Portugal, both because of its size and the importance of the engineering works being undertaken in the area. As a consequence, its geotechnical characteristics have been investigated in detail. It is an alluvial deposit composed of the weathered products of granite rock: quartz, mica and feldspar, the latter altering to kaolinite. Silt is the predominant grain size fraction and there is a local presence of organic matter and fragments of shells from marine organisms.
29.3.1 Geotechnical properties Figure 29.5 illustrates the most important characteristic of the deposit: the soil's variability. There is a large scatter in the physical parameters like w and y. Figure 29.6a presents further evidence of the variability, strongly suggesting that this is an intrinsic property of the soil rather than being an effect of some poor quality testing procedure. The variability is typical of almost every physical property represented, notably w, y, G and e. The degree of saturation (S) is, as expected, always quite close to 100 %. Considering that this is indirectly obtained from the values of other physical properties experimentally measured that also exhibit large variability, it gives additional support to believing the high quality of the results. This figure also portrays the variability of organic matter, which the author considers to be the main feature responsible for the variability found in the
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geotechnical properties. The organic constituent of the soil also strongly increases the plasticity, as can be deduced from the plastic and liquid limits not being compatible with the grain size and mineralogical composition.
.
Triaxial I Triaxial I1
O Oedometer I 0 Oedometer I1
X Field tests
Figure 29.5. Natural variability of some geotechnical properties (Coelho, 2000)
29.3.2 Stress history and consolidation properties Figure 29.6b presents the geotechnical values measured for several parameters describing the stress history and the consolidation characteristics of the Baixo Mondego soil. As expected from its geological history, the deposit has a desiccated crust at the surface where the soil is clearly over-consolidated. Within this relatively hard crust, OCR values possess a scatter that surely increases the natural variability of the soil's physical condition, although its effects are not expected to exceed those resulting from the presence of organic matter. In addition, this figure shows that the parameters related to the consolidation properties of the deposit also present a significant variability. Even when considering intrinsic compressibility parameters like C, or C,, the range of values measured within the deposit is quite perceptible. C,, for example, varies between 0.41 and 1.24, values that are not only extremely high in comparison with commonly occurring soils but are also quite distinctive. The high variability can be found in other properties, such as the coefficient of secondary consolidation (C,), the coefficient of permeability (k), and the undrained shear strength (c,). The latter strength parameter is the geotechnical characteristic most commonly required to quantify engineering behaviour. The field vane test is a frequently used method to measure its value.
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(a) physical and identification properties OCR
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c, c,
(b) stress history and consolidation properties Figure 29.6. General properties of the Baixo Mondego soil (Coelho, 2000)
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Figure 29.7a portrays the values of the undrained shear strength obtained during the ground investigation, again showing very high scatter. However, inspection of the results shows that the rotation of the vane at failure exceeded 90" on several tests. This is unacceptable and is probably caused by fragments of shells jamming the vane. Selecting only those results which failed below 90°, Figure 29.6b yields a more stable pattern of c,.
I (a) all values measured in the field
I- vane tests under recent embankment 0 11- vane tests under old embankment
(b) analysis of selected values
Figure 29.7. Undrained shear strength as measured by the vane test (Coelho, 2000)
Although the increase in undrained shear strength with time following embankment construction clearly results from a time-dependent increase in the soil effective stress, some uncertainty still remains regarding the values measured. Perhaps this is due to the unavoidable effects of disturbance, especially at shallower depth where the soil sensitivity tends to increase with lower OCR.
29.4 Practical implications The results suggest that, in practice, a greater number of tests is required to achieve equal confidence concerning the experimental results obtained in soft soils than with stiffer materials, exacerbated by the (usual) presence of organic matter. The consequence is a high scatter on geotechnical properties. Care has to be taken
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to include an assessment of the effects of disturbance that could otherwise lead to quite misleading conclusions. When characterising a soft soil, the presence of organic matter can significantly alter the behaviour that would be anticipated for a comparable inorganic soil, even if the organic matter is only a minor constituent.
Acknowledgments The author would like to express his gratitude to Professor Luis Lemos, who supervised the experimental research, and CIEC and FCT that financially supported the research through project PRAXIS/C/ECM/CEG/l4259/98.
References Amaryan LS (1993) Soft soil properties and testing methods. Geotechnlka 7, A.A. Balkema, Rotterdam, Brookfield Coelho PLdeF (2000) Geotechnical characterisation of soft soils - study of the experimental site of Quinta do Foja. MSc thesis. Coimbra: University of Coimbra. (in Portuguese) Haan EJD, Amir LSFE (1994) A simple formula for final settlement of surface loads on peat. Advances in Understanding and Modelling the Mechanical Behaviour of Peat. Haan, Tennaat and Edil, Editors, Netherlands, A.A. Balkema, Rotterdam, Brookfield Hight DW, Bond AJ, Legge JD (1992) Characterisation of the Bothkennar clay: an overview. Ge'otechnique,42(2), 303-347
30 Analysing uncertainty when modelling geological structures Pauli Saksa, Pirjo Hella, Jorma Nummela Fintact Oy, Finland
30.1 Introduction There is uncertainty when modelling geological structures located in Precambrian crystalline bedrock. In Finland and Sweden this is typically granite or gneiss in composition and subjected to polyphase metamorphism. The uncertainty in lithology and tectonic structure has been considered for three sites ('A', 'B' and 'C') as part of a comprehensive study undertaken for the Swedish Nuclear Fuel Waste Management Co. (SKB) (Saksa and Nummela, 1998). This has been supplemented by information from the Olkiluoto site modelling, in Finland, and discussed in the context of fracturing and its intensity (Ch. 4.4 in Vaittinen et al., 200 1).
30.2 Sources of uncertainty Uncertainty is an inherent characteristic of all ground investigations. The major sources originate from measurements (errors in data, sampling limitations, scale variation) and concept (structural geometry and properties, ambiguous geometric or parametric solutions). These may be considered as follows:
Geological concept (evolution, deformation phase, age, relationships, descriptive forms, analogues, style related to cartographic presentation) Measurement (sampling limitations, spatial distribution, coverage) Structural geometry (zonation, continuity, planarity, relationship to lithology, anisotropy, system complexity) Structural properties (generic data, discrete data, variability) Error (in observations, in interpretation, in correlation, with integration) Inconsistency (definitions missing, varying cut-off values, subjective lineaments, representative vs. interesting)
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Scale (transfer between scales, generalisation) Unknown lithology or structure (resulting from unsampled volumes, site conditions, available investigation methods, noise).
30.3 Methodology The principal approach has utilised the classical expert review of site investigation data and modelling results. To supplement this, the following treatment of uncertainties was carried out: Compilation of survey tables and maps (with the help of GIs and CAD software) Comparison with tables and maps Comparison of concepts and site definitions Assessment of borehole data: deterministically and statistically Fieldwork to evaluate the site and overburden conditions Data quality checks of original data, methods and interpretations Calculation of thematic uncertainty indices for all sites. A difficulty recognised at the outset lies in detail, in source data and interpretation. An example which depicts both varying levels of detail and scale variation is presented in Figure 30.1. On the left, the map at a regional scale (-10 km) portrays summarised results and no uncertainty. In the middle, borehole data from fracture zone intersections displayed at a site scale (1 km) show much more detail. It is evident that a number of possible interconnections and interpretations is possible. On the right, fracture orientation data at a detailed scale (10 m) permits several alternative interpretations to be made for one particular borehole section; the degree of associated uncertainty is thus considerable. Another source of uncertainty relates to the concept criteria applied. A specific example is the fracture zone criteria used in site scale modelling. Fracture frequency is a parameter commonly used to delineate fracture zones. At sites 'A' and 'C' a cut-off value of 10 fractureslm was utilised, at site 'B' the value was 6 fractureslm. This means that in a similar fracture state, the site 'B' model will include more fracture zones than the other sites. If site 'B' is used as a reference, sites 'A' and 'C' will have higher uncertainty (more missing structures) regarding the determination of the fracture zones. Fracture frequency may be related also to the size of structures (length, continuity) and, if the criteria used varies, this may show up in the geometry of the structure.
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Figure 30.1. Visualisation at three different scales demonstrating how uncertainty can become hidden in the detail
30.4 Case Study 30.4.1 Main site characteristics At site 'A' the development of a regional model has been thoroughly performed. At the site scale, a major source of uncertainty concerns the absence of a clear definition of the target area. Structures encountered in the boreholes are well explained and an interdisciplinary approach to interpretation has taken place. A large part of site 'A' was covered by sea (40 %), inhibiting geological and the geophysical studies of the bedrock except for airborne magnetic and seismic refraction. It has certainly had a masking influence. Model development at site 'B' used half the available methods at a regional scale. Mostly this means that some general methods have not been used, but exceptionally it may mean that acquired data has not been used. The site area was thus unclear and a complicated survey pattern evolved (Figure 30.2). The legend illustrates the situation normally met at a site: boreholes were placed to investigate anomalous situations or where average ground was anticipated. Geological investigations produce points, lines or areas. Such surveys are often conducted as part of the characterisation programme but some surveys have somewhat specific goals and spatial targets. Figure 30.2 contains uncertainties related to coverage and density of investigation, indicated in a qualitative manner. The model for site 'B' accounts for half the fractured sections encountered in boreholes; consequently large amounts of data remain without being interpreted. Regional models for sites 'B' and 'C'contain relatively large uncertainties due to the investigation methodology and limited experience available at the time of the investigation (1970s-80s). At a site scale, six additional structures were proposed at both sites. Uncertainty was detected in the form of fractured sections along the boreholes that had not been interpreted. At site 'C', the regional lithology was a possible source of additional structures that had not been analysed. The
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lithological properties were also not known (because they had not been investigated), for instance major dolerite dykes.
Figure 30.2. Surface map of ground investigation at site B. Uncertainty distributions are illustrated for example areas
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During this study, no major differences in concept between the sites were noted. One source of uncertainty in the site modelling was the apparent absence of structures at a scale of 10-300 m. In practise these correspond to features such as long individual fractures, fracture swarms and minor fracture zones (typically a few metres thick). Such fractures were partly incorporated at site 'A' by 'hydraulic structures', but not recorded at sites 'B' or 'C'. Fractures at a smaller scale (size
